This paper evaluates different optical and synthetic aperture radar (SAR) image fusion methods applied to open-access Sentinel images with global coverage. The objective of this research was to evaluate the potential of image fusion methods to get a greater visual difference in land cover, especially in oil palm crops with natural forest areas that are difficult to differentiate visually. The application of the image fusion methods: Brovey (BR), high-frequency modulation (HFM), Gram-Schmidt (GS), and principal components (PC) was evaluated on Sentinel-2 optical and Sentinel-1 SAR images using a cloud computing environment. The results show that the application of the implemented optical/SAR image fusion methods allows the creation of a synthetic image with the characteristics of both data sources. The multispectral information provided by the optical image and information associated with the geometry and texture/roughness of the land covers, provided by the SAR image, allows a greater differentiation in the visualization of the various land covers, achieving a better understanding of the study area. The fusion methods that visually presented greater characteristics associated with the SAR image were the BR and GS methods. The HFM method reached the best statistical indicators; however, this method did not present significant visual changes in the SAR contribution.
Comparing canopy density measurement from UAV and hemispherical photography: ...IJECEIAES
UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots.
Typical Meteorological Year Report for CSP, CPV and PV solar plantsIrSOLaV Pomares
This technical report analyzes the solar resource available at a site in Northern Cape, South Africa selected to host a solar thermal power plant. It presents a typical meteorological year (TMY) developed using 12 years of hourly solar radiation data for the site. The TMY is generated using a methodology that selects the most representative month from each year for key meteorological variables. It is comprised of months from 2007 to 2010 that best match the long-term averages for global horizontal and direct normal solar radiation at the site. The TMY and long-term averages are presented and show a close match in monthly and daily solar radiation patterns for use in modeling solar power production at the site.
Summary: The province of Mendoza can administrate water using digital tools that are used for the Science of Earth, and that way to optimize the use of resource, with an intrinsic impact on Economic Science, it is said projections on its productive array. Thus, early development of abilities on this kind of tools that takes part of the so-called Administration 4.0, allows to the professional future of Economic Science and more specifically to the Public Administrators, being more competitive, keeping up online with the new demands that visualize by the digital revolution that are undertaking.
This document summarizes a study that assessed flood damage to agricultural lands in Bangladesh using earth observation techniques. Sentinel-1 radar data and Sentinel-2 optical data were used to map flooded areas during a 2017 flood event and estimate damage to paddy fields. Polarimetry and spectral/spatial analysis techniques were applied to the radar and optical images respectively to extract inundated areas. Over 4,700 hectares of damaged croplands were identified from the radar data and over 3,900 hectares from the optical data, resulting in estimated economic losses of 18 million and 14.8 million respectively. Sensitivity analysis was performed to select the best parameters for flood mapping and damage assessment.
Chronological Calibration Methods for Landsat Satellite Images iosrjce
This document describes methods for chronologically calibrating Landsat satellite images to account for differences when images are taken days apart. It discusses correcting ETM+ images for scan line failures and converting digital numbers to reflectance. Two methods are proposed to remove phenological effects between Landsat 7 and 8 images taken 8 days apart: linear regression and cross-correlation. Image classification using the visible red and near infrared bands is used to validate the correction methods by comparing land cover detection in study area images.
Paddy field classification with MODIS-terra multi-temporal image transformati...IJECEIAES
This paper presents the paddy field classification model using the approach based on periodic plant life cycle events and how these elevations in climate as well as habitat factors, such as elevation. The data used are MODIS-Terra two tiles of H28v09 and H29v09 of 2016, consist of 46 series of 8-daily data, with 500 meter resolution in Java region. The paddy field classification method based on the phenological model is done by Maximum Likelihood on the transformed annual multi-temporal image of the reflectance data, index data, and the combination of reflectance and index data. The results of the study showed that, with the reference of the Paddy Field Map from the Ministry of Agriculture (MoA), the overall accuracies of the paddy field classification results using the combination of reflectance and index data provide the highest (85.4%) among the reflectance data (83.5%) and index data (81.7%). The accuracy levels were varied; these depend on the slope and the types of paddy fields. Paddy fields on the slopes of 0-2% could be well identified by MODIS-Terra data, whereas it was difficult to identify the paddy fields on the slope >2%. Rain-fed lowland paddy field type has a lower user accuracy than irrigated paddy fields. This study also performed correlation (r2) between the analysis results and the statistical data based on district and provincial boundaries were >0.85 and >0.99 respectively. These correlations were much higher than the previous study results, which reached 0.49-0.65 (hilly-flat areas of county-level), and 0.80-0.88 (hilly-flat areas of provincial level) for China, and reached 0.44 for Indonesia.
REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS AM Publications
Remote sensing technology's increasing accessibility helps us observe research and learn about our globe in ways we could only imagine a generation ago. Guides to profound knowledge of historical, conceptual and practical uses of remote sensing which is increasing GIS technology. This paper will go briefly through remote sensing benefits, history, technology and the GIS and remote sensing integration and their applications. Remote sensing (RS) is used in mapping the predicted and actual species and dominates the ecosystem canopy.
Comparing canopy density measurement from UAV and hemispherical photography: ...IJECEIAES
UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots.
Typical Meteorological Year Report for CSP, CPV and PV solar plantsIrSOLaV Pomares
This technical report analyzes the solar resource available at a site in Northern Cape, South Africa selected to host a solar thermal power plant. It presents a typical meteorological year (TMY) developed using 12 years of hourly solar radiation data for the site. The TMY is generated using a methodology that selects the most representative month from each year for key meteorological variables. It is comprised of months from 2007 to 2010 that best match the long-term averages for global horizontal and direct normal solar radiation at the site. The TMY and long-term averages are presented and show a close match in monthly and daily solar radiation patterns for use in modeling solar power production at the site.
Summary: The province of Mendoza can administrate water using digital tools that are used for the Science of Earth, and that way to optimize the use of resource, with an intrinsic impact on Economic Science, it is said projections on its productive array. Thus, early development of abilities on this kind of tools that takes part of the so-called Administration 4.0, allows to the professional future of Economic Science and more specifically to the Public Administrators, being more competitive, keeping up online with the new demands that visualize by the digital revolution that are undertaking.
This document summarizes a study that assessed flood damage to agricultural lands in Bangladesh using earth observation techniques. Sentinel-1 radar data and Sentinel-2 optical data were used to map flooded areas during a 2017 flood event and estimate damage to paddy fields. Polarimetry and spectral/spatial analysis techniques were applied to the radar and optical images respectively to extract inundated areas. Over 4,700 hectares of damaged croplands were identified from the radar data and over 3,900 hectares from the optical data, resulting in estimated economic losses of 18 million and 14.8 million respectively. Sensitivity analysis was performed to select the best parameters for flood mapping and damage assessment.
Chronological Calibration Methods for Landsat Satellite Images iosrjce
This document describes methods for chronologically calibrating Landsat satellite images to account for differences when images are taken days apart. It discusses correcting ETM+ images for scan line failures and converting digital numbers to reflectance. Two methods are proposed to remove phenological effects between Landsat 7 and 8 images taken 8 days apart: linear regression and cross-correlation. Image classification using the visible red and near infrared bands is used to validate the correction methods by comparing land cover detection in study area images.
Paddy field classification with MODIS-terra multi-temporal image transformati...IJECEIAES
This paper presents the paddy field classification model using the approach based on periodic plant life cycle events and how these elevations in climate as well as habitat factors, such as elevation. The data used are MODIS-Terra two tiles of H28v09 and H29v09 of 2016, consist of 46 series of 8-daily data, with 500 meter resolution in Java region. The paddy field classification method based on the phenological model is done by Maximum Likelihood on the transformed annual multi-temporal image of the reflectance data, index data, and the combination of reflectance and index data. The results of the study showed that, with the reference of the Paddy Field Map from the Ministry of Agriculture (MoA), the overall accuracies of the paddy field classification results using the combination of reflectance and index data provide the highest (85.4%) among the reflectance data (83.5%) and index data (81.7%). The accuracy levels were varied; these depend on the slope and the types of paddy fields. Paddy fields on the slopes of 0-2% could be well identified by MODIS-Terra data, whereas it was difficult to identify the paddy fields on the slope >2%. Rain-fed lowland paddy field type has a lower user accuracy than irrigated paddy fields. This study also performed correlation (r2) between the analysis results and the statistical data based on district and provincial boundaries were >0.85 and >0.99 respectively. These correlations were much higher than the previous study results, which reached 0.49-0.65 (hilly-flat areas of county-level), and 0.80-0.88 (hilly-flat areas of provincial level) for China, and reached 0.44 for Indonesia.
REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS AM Publications
Remote sensing technology's increasing accessibility helps us observe research and learn about our globe in ways we could only imagine a generation ago. Guides to profound knowledge of historical, conceptual and practical uses of remote sensing which is increasing GIS technology. This paper will go briefly through remote sensing benefits, history, technology and the GIS and remote sensing integration and their applications. Remote sensing (RS) is used in mapping the predicted and actual species and dominates the ecosystem canopy.
Land use/land cover classification using machine learning modelsIJECEIAES
An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers.
Application Of Deep Learning On UAV-Based Aerial Images For Flood DetectionTodd Turner
This document discusses using deep learning techniques on aerial images captured by UAVs to detect floods in near real-time. The key points are:
1) UAVs can capture high-resolution images faster than satellites and do not require internet connectivity, overcoming issues with current flood detection methods.
2) A case study tested detecting buildings and roads from UAV images using Haar cascade classification, achieving 91% and 94% accuracy.
3) A deep learning model was trained on the detected landmarks to classify images as flooded or non-flooded, achieving an overall accuracy of 91%.
Separability Analysis of Integrated Spaceborne Radar and Optical Data: Sudan ...rsmahabir
Abstract-The purpose of this study was to determine via spectral separability using divergence measures the best individual and combinations of various numbers of bands for five land cover/ land use classes along the Blue Nile in Sudan. The data for this analysis were a stack of 15 layers including RADARSAT-2 C-band and PALSAR L-band quad-polarized radar registered with ASTER optical data, as well as four variance texture measures extracted from the RADARSAT-2 images. Spectral signatures were obtained for each class and examined by various separability measures. This examination is useful for better understanding the relative value of different types of remote sensing data and best band combinations for possible visual analysis and for improving land cover/ land use classification accuracy. Results show that the best single band for analysis was the RADARSAT-2 VH variance texture measure. The best pair of bands was the ASTER visible red and the RADARSAT-2 HV variance texture, which also included the PALSAR VH band for the best three band combination, all bands being very different data types. Further, based upon the divergence values, only eight bands are needed to achieve maximum separation between land cover/ land use classes. Beyond this point, classification accuracy is expected to decrease, with as few as six bands needed to reach viable classification accuracy.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes a research paper that classified multi-date remote sensing images using NDVI values. It discusses how NDVI values were calculated from Terra satellite imagery using red and infrared band values. A similarity measure formula was proposed to classify images based on comparing NDVI values of unknown images to reference images. The formula measured similarity between image windows using sum of absolute differences of NDVI values. Five Terra images from different dates were classified into 20 reference classes using this approach.
A Review of Change Detection Techniques of LandCover Using Remote Sensing Dataiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document provides an overview of change detection techniques for landcover using remote sensing data. It begins by discussing issues that can impact change detection accuracy, such as image acquisition parameters, viewing geometry, and radiometric correction. It then categorizes change detection techniques as either pre-classification or post-classification. Pre-classification techniques extract minimal change information, while post-classification techniques separate changes in more detail but may miss subtle within-class changes. Object-based techniques that segment images into objects before analysis are also discussed as a recent development.
DUAL-CHANNEL MODEL FOR SHALLOW WATER DEPTH RETRIEVAL FROM WORLDVIEW-3 IMAGERY...Luhur Moekti Prayogo
This research aims to estimate shallow water depth using Worldview 3 satellite imagery and dual-channel models in Karimunjawa waters, Central Java – Indonesia. To build dual-channel models, we used spectral data that had been validated in the field. Twenty-three depth data were recorded synchronous to the spectral data used in forming the semianalytical dual-channel models. Twelve models were tested using 633 depth data with a non-linear model using multiple polynomial regression analysis degrees 1 and 2. This research has shown that the proposed model has been confirmed to improve depth accuracy. Models using blue and green channels of Worldview 3 image result in good accuracies especially for estimating depths with interval from 5 to 20 meters with RMSE of 1,592 meters (5–10 meters), 2,099 meters (10–15 meters), and 1,239 meters (15–20 meters). The wavelengths of two channels have a low absorption rate to penetrate deeper waters than other wavelengths. The research also finds out that there are still models that meet the IHO standard criteria.
Tarımsal Toprak Haritalama'da Jeofizik MühendisliğiAli Osman Öncel
1) The document discusses a study evaluating the reliability and reproducibility of electromagnetic induction (EMI) data collection.
2) The study compared data from two identical EMI instruments, the calibration methods of different individuals, and variations in calibration height.
3) The results showed significant differences between instruments, calibrations, and heights. This demonstrates the need for standardization of EMI data collection procedures to ensure reliable and reproducible data.
Greetings all,
Nowadays, several datasets are -or will be- available in a near future to improve operational forecasting in most aspects, like the
ocean dynamics modeling, and the assimilation efficiency, that aims now to optimize the combination of temperature/salinity in
situ profiles, drifter's velocities, and sea surface height deduce from altimeter's data and GRACE or future Goce geoid. But also
strengthen forecasting system's applications, like the climate monitoring. For all these issues, an optimal use of ocean data,
always too sparse and not enough numerous, is mandatory.
Such studies are at the heart of this Newsletter issue. It begins with a Rio M.H. and Hernandez F. review of the Goce Mission,
dedicated to focus and document the shortest scales of the Earth's gravity field. Goce satellite is due to fly in December 2007.
With the next article Guinéhut S. and Larnicol G. investigate the influence of the in situ temperature profiles sampling on the
thermosteric sea level estimation. They show that the impact is not negligible, and can introduce large errors in the estimation. In
the second article, Benkiran M. and Greiner E. are evaluating the benefits of the drifter's velocities assimilation in the Mercator
Océan 1/3° Tropical and North Atlantic operational system. A description of the assimilation scheme upgrade to take into account
velocity control is given. Castruccio F. & al. describe in the third article the performance of an improved MDT reference for
altimetric data assimilation. They concentrate their study on the Tropical Pacific Ocean. Finally, the Newsletter comes to an end
with the Benkiran M. article. In his study, based on the 1/3° Mercator system, the impact of several altimeters data on the
assimilation performance is assessed
Have a good read
Influence of land use land cover in simulating the thunderstorm event using ...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Automatic traffic light controller for emergency vehicle using peripheral int...IJECEIAES
Traffic lights play such important role in traffic management to control the traffic on the road. Situation at traffic light area is getting worse especially in the event of emergency cases. During traffic congestion, it is difficult for emergency vehicle to cross the road which involves many junctions. This situation leads to unsafe conditions which may cause accident. An Automatic Traffic Light Controller for Emergency Vehicle is designed and developed to help emergency vehicle crossing the road at traffic light junction during emergency situation. This project used Peripheral Interface Controller (PIC) to program a priority-based traffic light controller for emergency vehicle. During emergency cases, emergency vehicle like ambulance can trigger the traffic light signal to change from red to green in order to make clearance for its path automatically. Using Radio Frequency (RF) the traffic light operation will turn back to normal when the ambulance finishes crossing the road. Result showed the design is capable to response within the range of 55 meters. This project was successfully designed, implemented and tested.
The document presents research using multi-temporal COSMO-SkyMed SAR data for land cover classification and surface parameter retrieval over agricultural sites. Algorithms were developed for classification, leaf area index retrieval, and soil moisture content retrieval. The algorithms were applied to a 2010 dataset over Foggia, Italy, showing potential for crop classification, wheat leaf area index mapping, and soil moisture mapping of bare fields using X-band SAR. Future work involves validating the algorithms and assessing their improvement of land process models when coupled with SAR-derived information.
Land Use/Land Cover Mapping Of Allahabad City by Using Remote Sensing & GIS IJMER
The present study was carried out to produce and evaluate the land use/land cover maps by on
screen visual interpretation. The studies of land cover of Allahabad city (study area) consist of 87517.47 ha
out of which 5500.35 ha is build up land (Urban / Rural) Area. In this respect, the Build up land (Urban /
Rural) area scorers 6.28% of the total area. It has also been found that about 17155.001ha (19.60 %) of
area is covered by current fallow land. The double/triple crop land of 30178.44ha (34.84%). The area
covered by gullied / ravines is 1539.20 ha (1.75 %) and that of the kharif crop land is 2828.00 ha (3.23 %).
The area covered by other wasteland is 2551.05ha (2.91%). Table 4.1 shows the area distribution of the
various land use and land cover of Allahabad city.
This document summarizes the IrSOLaV methodology for estimating solar radiation from satellite images. The methodology uses geostationary satellite images and atmospheric data as inputs. Satellite images provide information on cloud cover characteristics, while atmospheric data includes parameters like Linke turbidity factor. A cloud index is computed from the satellite images and related to clear sky index to estimate solar radiation. Validation shows the methodology achieves a 12% RMSE for hourly solar radiation estimates compared to ground measurements.
Landuse landcover and ndvi analysis for halia catchmentIAEME Publication
This document summarizes a study analyzing land use/land cover changes and normalized difference vegetation index (NDVI) for the Halia catchment area in India over several decades using remote sensing data. Medium to high resolution Landsat satellite imagery from 1975, 1989, and 2001 was processed to create land use/land cover maps and NDVI maps for the area. The objective was to examine changes in cropped area and land use/land cover patterns over time and understand the implications for the local environment.
CLEARMiner: Mining of Multitemporal Remote Sensing ImagesEditor IJCATR
A new unsupervised algorithm, called CLimate and rEmote sensing Association patterns Miner, for mining association patterns on
heterogeneous time series from climate and remote sensing data, integrated in a remote sensing information system is developed to improve the
monitoring of sugar cane fields. The system, called RemoteAgri, consists of a large database of climate data and low-resolution remote sensing
images, an image pre-processing module, a time series extraction module, and time series mining methods. The time series mining method
transforms series to symbolic representation in order to identify patterns in a multitemporal satellite images and associate them with patterns in
other series within a temporal sliding window. The validation process was achieved with agro climatic data and NOAA-AVHRR images of
sugar cane fields. Rules generated by the new algorithm show the association patterns in different periods of time in each time series, pointing to
a time delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast without having the burden
of dealing with many data charts. This new method can be used by agro meteorologists to mine and discover knowledge from their long time
series of past and forecasting data, being a valuable tool to support their decision-making process.
Optimization of Parallel K-means for Java Paddy Mapping Using Time-series Sat...TELKOMNIKA JOURNAL
Spatiotemporal analysis of MODIS Vegetation Index Imagery widely used for vegetation
seasonal mapping both on forest and agricultural site. In order to provide a long -terms of vegetation
characteristic maps, a wide time-series images analysis is needed which require high-performance
computer and also consumes a lot of energy resources. Meanwhile, for agriculture monitoring purpose in
Indonesia, that analysis has to be employed gradually and endlessly to provide the latest condition of
paddy field vegetation information. This research is aimed to develop a method to produce the optimized
solution in classifying vegetation of paddy fields that diverse both spatial and temporal characteristics. The
time-series EVI data from MODIS have been filtered using wavelet transform to reduce noise that caused
by cloud. Sequential K-means and Parallel K-means unsupervised classification method were used in both
CPU and GPU to find the efficient and the robust result. The developed method has been tested and
implemented using the sample case of paddy fields in Java Island. The best system which can
accommodate of the extend-ability, affordability, redundancy, energy-saving, maintainability indicators are
ARM-based processor (Raspberry Pi), with the highest speed up of 8 and the efficiency of 60%.
IRJET- Land Use & Land Cover Change Detection using G.I.S. & Remote SensingIRJET Journal
This document discusses land use and land cover change detection in Vadodara, India between 1998 and 2008 using remote sensing and GIS techniques. Specifically, it analyzed Landsat satellite images from those two decades to map and classify land use, including built up area, vegetation, vacant land, and water bodies. The methodology involved image preprocessing like geometric correction and radiometric normalization. Images were then enhanced and classified using both supervised and unsupervised classification. Comparing the classified maps from 1998 and 2008 allowed analyzing changes in land use over that 10-year period and calculating the rate of land consumption. The study aimed to provide information to urban planners for predicting future growth and avoiding problems associated with rapid urbanization.
A Review Of Different Approaches Of Land Cover MappingJose Katab
This document reviews different approaches for land cover mapping, including artificial neural networks (ANNs), fuzzy logic, supervised/unsupervised classification, and maximum likelihood. It discusses how each approach has been applied in previous studies for land cover classification using remote sensing data. The document also examines common problems in remote sensing image classification, such as mixed pixels, and different methods that have been proposed and used to address these issues, such as maximum likelihood classification and fuzzy classifiers. Overall, the review analyzes and compares algorithms for land cover classification and evaluates methods for overcoming problems encountered during the classification process.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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Ähnlich wie Evaluation of optical and synthetic aperture radar image fusion methods: a case study applied to Sentinel imagery
Land use/land cover classification using machine learning modelsIJECEIAES
An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers.
Application Of Deep Learning On UAV-Based Aerial Images For Flood DetectionTodd Turner
This document discusses using deep learning techniques on aerial images captured by UAVs to detect floods in near real-time. The key points are:
1) UAVs can capture high-resolution images faster than satellites and do not require internet connectivity, overcoming issues with current flood detection methods.
2) A case study tested detecting buildings and roads from UAV images using Haar cascade classification, achieving 91% and 94% accuracy.
3) A deep learning model was trained on the detected landmarks to classify images as flooded or non-flooded, achieving an overall accuracy of 91%.
Separability Analysis of Integrated Spaceborne Radar and Optical Data: Sudan ...rsmahabir
Abstract-The purpose of this study was to determine via spectral separability using divergence measures the best individual and combinations of various numbers of bands for five land cover/ land use classes along the Blue Nile in Sudan. The data for this analysis were a stack of 15 layers including RADARSAT-2 C-band and PALSAR L-band quad-polarized radar registered with ASTER optical data, as well as four variance texture measures extracted from the RADARSAT-2 images. Spectral signatures were obtained for each class and examined by various separability measures. This examination is useful for better understanding the relative value of different types of remote sensing data and best band combinations for possible visual analysis and for improving land cover/ land use classification accuracy. Results show that the best single band for analysis was the RADARSAT-2 VH variance texture measure. The best pair of bands was the ASTER visible red and the RADARSAT-2 HV variance texture, which also included the PALSAR VH band for the best three band combination, all bands being very different data types. Further, based upon the divergence values, only eight bands are needed to achieve maximum separation between land cover/ land use classes. Beyond this point, classification accuracy is expected to decrease, with as few as six bands needed to reach viable classification accuracy.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes a research paper that classified multi-date remote sensing images using NDVI values. It discusses how NDVI values were calculated from Terra satellite imagery using red and infrared band values. A similarity measure formula was proposed to classify images based on comparing NDVI values of unknown images to reference images. The formula measured similarity between image windows using sum of absolute differences of NDVI values. Five Terra images from different dates were classified into 20 reference classes using this approach.
A Review of Change Detection Techniques of LandCover Using Remote Sensing Dataiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document provides an overview of change detection techniques for landcover using remote sensing data. It begins by discussing issues that can impact change detection accuracy, such as image acquisition parameters, viewing geometry, and radiometric correction. It then categorizes change detection techniques as either pre-classification or post-classification. Pre-classification techniques extract minimal change information, while post-classification techniques separate changes in more detail but may miss subtle within-class changes. Object-based techniques that segment images into objects before analysis are also discussed as a recent development.
DUAL-CHANNEL MODEL FOR SHALLOW WATER DEPTH RETRIEVAL FROM WORLDVIEW-3 IMAGERY...Luhur Moekti Prayogo
This research aims to estimate shallow water depth using Worldview 3 satellite imagery and dual-channel models in Karimunjawa waters, Central Java – Indonesia. To build dual-channel models, we used spectral data that had been validated in the field. Twenty-three depth data were recorded synchronous to the spectral data used in forming the semianalytical dual-channel models. Twelve models were tested using 633 depth data with a non-linear model using multiple polynomial regression analysis degrees 1 and 2. This research has shown that the proposed model has been confirmed to improve depth accuracy. Models using blue and green channels of Worldview 3 image result in good accuracies especially for estimating depths with interval from 5 to 20 meters with RMSE of 1,592 meters (5–10 meters), 2,099 meters (10–15 meters), and 1,239 meters (15–20 meters). The wavelengths of two channels have a low absorption rate to penetrate deeper waters than other wavelengths. The research also finds out that there are still models that meet the IHO standard criteria.
Tarımsal Toprak Haritalama'da Jeofizik MühendisliğiAli Osman Öncel
1) The document discusses a study evaluating the reliability and reproducibility of electromagnetic induction (EMI) data collection.
2) The study compared data from two identical EMI instruments, the calibration methods of different individuals, and variations in calibration height.
3) The results showed significant differences between instruments, calibrations, and heights. This demonstrates the need for standardization of EMI data collection procedures to ensure reliable and reproducible data.
Greetings all,
Nowadays, several datasets are -or will be- available in a near future to improve operational forecasting in most aspects, like the
ocean dynamics modeling, and the assimilation efficiency, that aims now to optimize the combination of temperature/salinity in
situ profiles, drifter's velocities, and sea surface height deduce from altimeter's data and GRACE or future Goce geoid. But also
strengthen forecasting system's applications, like the climate monitoring. For all these issues, an optimal use of ocean data,
always too sparse and not enough numerous, is mandatory.
Such studies are at the heart of this Newsletter issue. It begins with a Rio M.H. and Hernandez F. review of the Goce Mission,
dedicated to focus and document the shortest scales of the Earth's gravity field. Goce satellite is due to fly in December 2007.
With the next article Guinéhut S. and Larnicol G. investigate the influence of the in situ temperature profiles sampling on the
thermosteric sea level estimation. They show that the impact is not negligible, and can introduce large errors in the estimation. In
the second article, Benkiran M. and Greiner E. are evaluating the benefits of the drifter's velocities assimilation in the Mercator
Océan 1/3° Tropical and North Atlantic operational system. A description of the assimilation scheme upgrade to take into account
velocity control is given. Castruccio F. & al. describe in the third article the performance of an improved MDT reference for
altimetric data assimilation. They concentrate their study on the Tropical Pacific Ocean. Finally, the Newsletter comes to an end
with the Benkiran M. article. In his study, based on the 1/3° Mercator system, the impact of several altimeters data on the
assimilation performance is assessed
Have a good read
Influence of land use land cover in simulating the thunderstorm event using ...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Automatic traffic light controller for emergency vehicle using peripheral int...IJECEIAES
Traffic lights play such important role in traffic management to control the traffic on the road. Situation at traffic light area is getting worse especially in the event of emergency cases. During traffic congestion, it is difficult for emergency vehicle to cross the road which involves many junctions. This situation leads to unsafe conditions which may cause accident. An Automatic Traffic Light Controller for Emergency Vehicle is designed and developed to help emergency vehicle crossing the road at traffic light junction during emergency situation. This project used Peripheral Interface Controller (PIC) to program a priority-based traffic light controller for emergency vehicle. During emergency cases, emergency vehicle like ambulance can trigger the traffic light signal to change from red to green in order to make clearance for its path automatically. Using Radio Frequency (RF) the traffic light operation will turn back to normal when the ambulance finishes crossing the road. Result showed the design is capable to response within the range of 55 meters. This project was successfully designed, implemented and tested.
The document presents research using multi-temporal COSMO-SkyMed SAR data for land cover classification and surface parameter retrieval over agricultural sites. Algorithms were developed for classification, leaf area index retrieval, and soil moisture content retrieval. The algorithms were applied to a 2010 dataset over Foggia, Italy, showing potential for crop classification, wheat leaf area index mapping, and soil moisture mapping of bare fields using X-band SAR. Future work involves validating the algorithms and assessing their improvement of land process models when coupled with SAR-derived information.
Land Use/Land Cover Mapping Of Allahabad City by Using Remote Sensing & GIS IJMER
The present study was carried out to produce and evaluate the land use/land cover maps by on
screen visual interpretation. The studies of land cover of Allahabad city (study area) consist of 87517.47 ha
out of which 5500.35 ha is build up land (Urban / Rural) Area. In this respect, the Build up land (Urban /
Rural) area scorers 6.28% of the total area. It has also been found that about 17155.001ha (19.60 %) of
area is covered by current fallow land. The double/triple crop land of 30178.44ha (34.84%). The area
covered by gullied / ravines is 1539.20 ha (1.75 %) and that of the kharif crop land is 2828.00 ha (3.23 %).
The area covered by other wasteland is 2551.05ha (2.91%). Table 4.1 shows the area distribution of the
various land use and land cover of Allahabad city.
This document summarizes the IrSOLaV methodology for estimating solar radiation from satellite images. The methodology uses geostationary satellite images and atmospheric data as inputs. Satellite images provide information on cloud cover characteristics, while atmospheric data includes parameters like Linke turbidity factor. A cloud index is computed from the satellite images and related to clear sky index to estimate solar radiation. Validation shows the methodology achieves a 12% RMSE for hourly solar radiation estimates compared to ground measurements.
Landuse landcover and ndvi analysis for halia catchmentIAEME Publication
This document summarizes a study analyzing land use/land cover changes and normalized difference vegetation index (NDVI) for the Halia catchment area in India over several decades using remote sensing data. Medium to high resolution Landsat satellite imagery from 1975, 1989, and 2001 was processed to create land use/land cover maps and NDVI maps for the area. The objective was to examine changes in cropped area and land use/land cover patterns over time and understand the implications for the local environment.
CLEARMiner: Mining of Multitemporal Remote Sensing ImagesEditor IJCATR
A new unsupervised algorithm, called CLimate and rEmote sensing Association patterns Miner, for mining association patterns on
heterogeneous time series from climate and remote sensing data, integrated in a remote sensing information system is developed to improve the
monitoring of sugar cane fields. The system, called RemoteAgri, consists of a large database of climate data and low-resolution remote sensing
images, an image pre-processing module, a time series extraction module, and time series mining methods. The time series mining method
transforms series to symbolic representation in order to identify patterns in a multitemporal satellite images and associate them with patterns in
other series within a temporal sliding window. The validation process was achieved with agro climatic data and NOAA-AVHRR images of
sugar cane fields. Rules generated by the new algorithm show the association patterns in different periods of time in each time series, pointing to
a time delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast without having the burden
of dealing with many data charts. This new method can be used by agro meteorologists to mine and discover knowledge from their long time
series of past and forecasting data, being a valuable tool to support their decision-making process.
Optimization of Parallel K-means for Java Paddy Mapping Using Time-series Sat...TELKOMNIKA JOURNAL
Spatiotemporal analysis of MODIS Vegetation Index Imagery widely used for vegetation
seasonal mapping both on forest and agricultural site. In order to provide a long -terms of vegetation
characteristic maps, a wide time-series images analysis is needed which require high-performance
computer and also consumes a lot of energy resources. Meanwhile, for agriculture monitoring purpose in
Indonesia, that analysis has to be employed gradually and endlessly to provide the latest condition of
paddy field vegetation information. This research is aimed to develop a method to produce the optimized
solution in classifying vegetation of paddy fields that diverse both spatial and temporal characteristics. The
time-series EVI data from MODIS have been filtered using wavelet transform to reduce noise that caused
by cloud. Sequential K-means and Parallel K-means unsupervised classification method were used in both
CPU and GPU to find the efficient and the robust result. The developed method has been tested and
implemented using the sample case of paddy fields in Java Island. The best system which can
accommodate of the extend-ability, affordability, redundancy, energy-saving, maintainability indicators are
ARM-based processor (Raspberry Pi), with the highest speed up of 8 and the efficiency of 60%.
IRJET- Land Use & Land Cover Change Detection using G.I.S. & Remote SensingIRJET Journal
This document discusses land use and land cover change detection in Vadodara, India between 1998 and 2008 using remote sensing and GIS techniques. Specifically, it analyzed Landsat satellite images from those two decades to map and classify land use, including built up area, vegetation, vacant land, and water bodies. The methodology involved image preprocessing like geometric correction and radiometric normalization. Images were then enhanced and classified using both supervised and unsupervised classification. Comparing the classified maps from 1998 and 2008 allowed analyzing changes in land use over that 10-year period and calculating the rate of land consumption. The study aimed to provide information to urban planners for predicting future growth and avoiding problems associated with rapid urbanization.
A Review Of Different Approaches Of Land Cover MappingJose Katab
This document reviews different approaches for land cover mapping, including artificial neural networks (ANNs), fuzzy logic, supervised/unsupervised classification, and maximum likelihood. It discusses how each approach has been applied in previous studies for land cover classification using remote sensing data. The document also examines common problems in remote sensing image classification, such as mixed pixels, and different methods that have been proposed and used to address these issues, such as maximum likelihood classification and fuzzy classifiers. Overall, the review analyzes and compares algorithms for land cover classification and evaluates methods for overcoming problems encountered during the classification process.
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
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The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
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- Basics of IAM in AWS
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Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Evaluation of optical and synthetic aperture radar image fusion methods: a case study applied to Sentinel imagery
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 3, June 2023, pp. 2778~2787
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i3.pp2778-2787 2778
Journal homepage: http://ijece.iaescore.com
Evaluation of optical and synthetic aperture radar image fusion
methods: a case study applied to Sentinel imagery
Jose Manuel Monsalve-Tellez1,2
, Yeison Alberto Garcés-Gómez1
, Jorge Luis Torres-León2
1
Facultad de Ingeniería y Arquitectura, Universidad Católica de Manizales, Manizales, Colombia
2
Geomatics Section, Agronomy Research Program, Colombian Oil Palm Research Center-Cenipalma, Bogotá, Colombia
Article Info ABSTRACT
Article history:
Received May 6, 2022
Revised Sep 27, 2022
Accepted Oct 1, 2022
This paper evaluates different optical and synthetic aperture radar (SAR)
image fusion methods applied to open-access Sentinel images with global
coverage. The objective of this research was to evaluate the potential of
image fusion methods to get a greater visual difference in land cover,
especially in oil palm crops with natural forest areas that are difficult to
differentiate visually. The application of the image fusion methods: Brovey
(BR), high-frequency modulation (HFM), Gram-Schmidt (GS), and principal
components (PC) was evaluated on Sentinel-2 optical and Sentinel-1 SAR
images using a cloud computing environment. The results show that the
application of the implemented optical/SAR image fusion methods allows
the creation of a synthetic image with the characteristics of both data
sources. The multispectral information provided by the optical image and
information associated with the geometry and texture/roughness of the land
covers, provided by the SAR image, allows a greater differentiation in the
visualization of the various land covers, achieving a better understanding of
the study area. The fusion methods that visually presented greater
characteristics associated with the SAR image were the BR and GS methods.
The HFM method reached the best statistical indicators; however, this
method did not present significant visual changes in the SAR contribution.
Keywords:
Cloud computing
Image fusion methods
Land cover
Optical
Sentinel
Synthetic aperture radar
This is an open access article under the CC BY-SA license.
Corresponding Author:
Jose Manuel Monsalve-Tellez
Geomatics Section, Agronomy Research Program, Colombian Oil Palm Research Center-Cenipalma
Calle 98#70-91, Bogotá D.C., 111121, Colombia
Email: jmonsalve@cenipalma.org, jose.tellez@ucm.edu.co
1. INTRODUCTION
The significant advances presented in the field of remote sensing related to the development of
various sensors onboard satellites for earth observation increasingly enable studies that integrate the
combined use of multiple sensors to acquire more information from a particular area of interest [1]–[3].
These developments have made it possible to carry out various investigations related to the determination of
land cover, analyze the dynamics of change in different land uses [4], and monitor the growth of
deforestation patterns associated with the expansion of agro-industrial production zones using optical and
radar satellite platforms [5], [6]. The oil palm agroindustry has shown enormous growth in recent years due
to the great demand that the sector has today [7], causing negative impacts on different natural ecosystems
[8], especially in the ecosystems of the largest oil palm producing countries in the world such as Indonesia
and Malaysia [6]. In Colombia, the oil palm crop is represented by the “Federación Nacional de Cultivadores
de Palma de aceite-Fedepalma” and is positioned as the largest exporter of palm oil in the Americas and the
fourth largest worldwide [9], [10]. Despite the efforts to promote sustainable development of the sector,
ecosystems still have impacts, especially in the savanna region [11]. Official land cover maps in Colombia
2. Int J Elec & Comp Eng ISSN: 2088-8708
Evaluation of optical and synthetic aperture radar image fusion methods … (Jose Manuel Monsalve-Tellez)
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usually show confusion between the land cover associated with natural forests and oil palm plantations,
making it difficult to monitor deforestation caused by this agroindustry. Therefore, the application of remote
sensing for monitoring this productive sector has enormous potential, allowing the analysis of large land
areas [12].
Currently, it is possible to acquire information from various open-access satellite platforms
dedicated to earth observation, which have significantly increased their spatial and temporal resolutions, such
as the Sentinel satellites of the Copernicus program of the European Space Agency (ESA). This space
program provides information on the earth’s surface through a satellite monitoring system with global
coverage and revisits periods every six days, using its two operating satellites. This constellation includes
satellites with optical sensors, such as Sentinel-2 [13], which capture data associated with the portion of
radiation reflected from the earth’s surface, and satellites with synthetic aperture radar (SAR) sensors such as
Sentinel-1 [14], which captures data concerning the geometry, texture/roughness and water content of land
cover on the earth’s surface through the intensity of the backscattering signal to the radar sensor [15].
Combining optical and SAR data sources provides more information associated with the land covers in the
study area [1], [16].
Some authors have demonstrated the ability to link data from both optical and SAR data sources to
increase the quality of land cover determination by implementing synergistic methods [17], [18]. In these
investigations, better results were achieved in classifiers when using both sources of information than when
using them independently, evaluating diverse levels of land cover classification [19] and applying them in
monitoring the growth of oil palm plantations [7], [20]. Optical and SAR image fusion methods have
demonstrated the capacity of generating synthetic images with the characteristics of both sources for
identifying flooded areas [21] and improving the visual difference of water bodies from other land covers
[22]. Thus, it is essential to develop methods that facilitate the integration of various sources of satellite
information that are complementary, such as optical and SAR images.
The large volumes of spatial information provided by these global monitoring systems represent an
enormous potential for application in the solution of problems such as the continuous monitoring of land
cover dynamics over large areas and through cloud computing platforms such as Google Earth Engine
(GEE), it is possible to analyze them [18], [21]. Based on this, research has been developed through this
platform, where the great benefits it offers in developing projects that link information from different satellite
platforms have been exposed [20]. They have been implemented in determining the land cover of large
extensions of land [21] and focused on determining areas associated with oil palm growing [23]. In addition,
research has been carried out to discriminate the coverages related to this same crop from other types of
coverage such as forests and secondary vegetation using SAR images [24], where the potential of using these
sources for this purpose is demonstrated, such as the studies carried out by [7], [25], [26].
The objective of this research was to evaluate different image fusion methods applied to Sentinel
optical and SAR images that allow increasing the visual identification of different land covers, especially to
differentiate between oil palm plantations and natural forest covers. This research will provide a better
understanding of the study area and thus visually monitor the dynamics associated with this agribusiness.
2. RESEARCH METHOD
The project was conducted at the Palmar de la Vizcaína experimental station (CEPV), of the Oil
Palm Research Center-Cenipalma, located on the WGS84 geographic coordinates of 6º59’13” N,
73º40’62” O, in the municipality of Barrancabermeja, Santander, Colombia as shown in Figure 1. Oil palm
cultivation in Colombia is distributed in four palm zones, where the central palm zone has the second-largest
representation corresponding to 32% of the total area planted in the country [27]. Climatic and topographic
conditions are tropical, with mean annual temperatures of 27 ºC and mean annual rainfall of 1,600 mm.
January is the driest month, and October is the month with the highest precipitation, with relative humidity
above 80%. The topographic conditions are characterized by being a mostly flat area with slopes of less than
10%. This area has climatic conditions that favor the establishment of oil palm cultivation. Within the study
area, there are areas of natural forests characteristic of tropical regions described above, oil palm plantations,
grasslands dedicated mainly to livestock, bare soils, secondary vegetation, and some water bodies.
The images used for the project were selected from the ESA Copernicus program using the GEE
cloud computing platform. The cloud-free optical image from the Sentinel-2 satellites [13] acquired on
01/24/2019 with processing level 2A in descending orbit was used. For the generation of the SAR image with
a low speckle effect, images from the Sentinel-1 satellite acquired from 11/24/2018 to 03/24/2019 with
ground range detected (GRD) processing level in descending orbit was selected. This range of dates
corresponds to a dry season with a monthly rainfall of fewer than 90 millimeters, so the SAR images chosen
in this study present similar humidity conditions. In this case, eight SAR images acquired during the period
mentioned above were used.
3. ISSN: 2088-8708
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Figure 1. Location of the study area, central palm zone, Santander, Colombia
The methodology implemented is presented in Figure 2. As a first step, the SAR image must be
preprocessed to reduce the speckle effect of “salt and pepper” caused by the interaction of the radar signal
waves on the different textures and structural conformation of the land cover [28], [29]. For this process, we
applied the multitemporal speckle reduction methodology proposed by Quegan and Yu [30] and implemented
it in GEE by Mullissa et al. [31], which consists of edge noise reduction where low-intensity noise and
invalid data present in the scene are removed. Speckle filtering is performed by averaging the backscattering
decibel values current in several images with acquisition dates as close as possible, in this study case we use
the images acquired in the period mentioned above. The normalization process of the backscatter decibel
values of the SAR image is performed between 0 and 1 to be comparable with the values present in the
optical image [32].
Figure 2. The overall workflow used in this study is described in the pre-processing, image fusion process,
and evaluation of image fusion methods
The bands selected for the optical image are associated with the visual and near-infrared spectrum
from B2 to B8A, B9, and shortwave infrared B11 and B12. For the SAR case, the vertical emission and
horizontal reception (VH) polarization bands were selected from the SAR image. This band was set based on
the principle of wave interaction with surface objects [7]. Given the wavelength at which Sentinel-1 operates
(C-band of 5.55 cm) [14], the VH polarization presents a lower reflection when interacting with oil palm
4. Int J Elec & Comp Eng ISSN: 2088-8708
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leaves than with the leaves of trees in forests, which allows better differentiation between this coverage and
those associated with natural forests [33].
The image fusion methods evaluated in this research are Brovey (BR) method [34], the
high-frequency modulation (HFM) method [35], the Gram-Schmidt (GS) method [36], and the principal
components (PC) method [37]. For the statistical evaluation of optical/SAR image fusion results, widely
studied indexes are found in the literature [38], [39]. In this case, five statistical indicators are implemented to
evaluate the quality of the results: root mean square error (RMSE), standard deviation (SDT), peak
signal-to-noise ratio (PSNR), correlation coefficient (CC), and relative dimensionless global error in
synthesis (ERGAS) [40]. In (1) to (5), MS represents the original multispectral image, F represents the fused
image, and M and N are the total numbers of rows and columns, respectively.
Root mean square error (RMSE) (1) allows measurement of the error associated with two sets of
data; in this case, the results acquired from the fused image compared to the original image. SDT (2)
estimates the image’s contrast, where the higher the deviation value, the greater the richness of the
information available. The value of the dispersion of the data is calculated around its arithmetic to mean.
PSNR (3) measures the noise present in the fused image compared to the original image, where the higher the
value of this index, the lower the noise of the fused image. CC (4) evaluates the correlation between the
original image and the fused image, where a higher degree of correlation means a good result of the fusion
process between the images. ERGAS (5) measures the overall quality of the image fusion process. Unlike the
previous ones, this index performs the calculation considering all the bands of the fused image, resulting in a
value that the smaller it is, the better the fusion result.
RMSE =
1
M∗N
√∑ ∑ (F − MS)2
N
n=1
M
m=1 (1)
SDT = √
1
M∗N
∑ ∑ (F − F
̅)2
N
n=1
M
m=1 (2)
PSNR = 10log(
Fmax
2
1
M∗N
∑ ∑ (F−F
̅)2
N
n=1
M
m=1
) (3)
CC =
∑ ∑ [(F−F
̅)∗(MS−MS
̅̅̅̅̅)]
N
n=1
M
m=1
√∑ ∑ (F−F
̅)2
N
n=1
M
m=1 ∗ ∑ ∑ (MS−MS
̅̅̅̅̅)2
N
n=1
M
m=1
(4)
ERGAS = 100
h
l
√
1
n
∑ [
RSME(i)
Mean(i)
]
2
n
i=1 (5)
where in SDT, PSNR, and CC, 𝐹
̅ represents the average of the fused image values. In PSNR, 𝐹𝑚𝑎𝑥
2
represents
the maximum value in the fused image. In CC, 𝑀𝑆
̅̅̅̅ represents the multispectral image values. In ERGAS, ℎ
and 𝑙 are the spatial resolutions of the fused and original image respectively, 𝑛 is the number of bands in the
image, 𝑅𝑆𝑀𝐸(𝑖) represents the root mean squared error of the 𝑖 band and 𝑀𝑒𝑎𝑛(𝑖) represents the average of
the 𝑖 band.
3. RESULTS AND DISCUSSION
The first step was the preprocessing of the images. In the case of the optical image, since it is at
processing level 2A, corresponding to surface reflectivity values, no additional processing was required. In
the case of the SAR image, since it is at a GRD processing level, the multitemporal speckle reduction method
was applied, and a median filter with a 3×3 moving window was applied. Using the multitemporal speckle
reduction method, it was possible to create a SAR image in which the geometric and textural characteristics
associated with the different coverages present in the study area can be better appreciated. As well as the
results achieved by Quegan and Yu [30], the potential of using consecutive SAR images to reduce noise
caused by the speckle effect and thus expand the possibilities of using SAR data in various applications, such
as the fusion of data acquired from different satellite sources. The result of the multitemporal speckle
reduction is shown in Figure 3, where a single SAR image is presented on the left and the result of the
multitemporal speckle reduction method using several consecutive SAR images is shown on the right.
This process allows to create a SAR image with truly little speckle noise effect, visualizing better the
different coverages in the study area, allowing to achieve better results in the optical and SAR image fusion
methods.
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Figure 3. Multitemporal speckle reduction method applicated in the “VH” band of Sentinel-1 SAR image
3.1. Optical/SAR image fusion methods
This section shows the results of applying each optical and SAR image fusion method. For each of
the fusion methods implemented, a synthetic image of eleven bands with a spatial resolution of 10 m each
was created, containing the information from both sources. The result of the BR method shows an
enhancement in the texture of the coverages without affecting their geometries. Visually, the quality of the
resulting image allows differentiation of the different coverages present in the study area. A noticeable
differentiation was found between the coverages associated with water bodies provided by the SAR image
due to the specular interaction of the radar wave. Comparable results to that found by [28], [39], which
presented a notorious visual difference of water bodies with the other land covers when applying optical/SAR
image fusion methods.
In the case of HFM method, the high-pass (HP) and low-pass (LP) filters were first applied to the
normalized SAR image, which are the inputs required for the application of this fusion method.
Subsequently, the HFM method was calculated, finding a very high similarity with the original image. The
enhancements associated with the texture of the coverages are not as noticeable as in the previous method.
The results achieved by [29], [41] demonstrate the efficiency of the application of HP and LP filters in image
fusion methods for the reduction of noise caused by speckles in the SAR image, which is reflected in the
result of this research. It has been shown that using these filters in image fusion methods significantly
improves the clarity of the objects in the study area [42]. However, an improvement in the sharpness of the
image is evidenced, where differentiation between the several types of coverages and linear objects present in
the scene, such as access roads and delimitation of the lots within the experimental station, is due to the
contribution of the enhancement of the HP filter in the fusion method.
The image generated by the GS method enhances the characteristics of the SAR image, such as the
texture associated with the coverages, and visual differences between them can be appreciated. As a result of
the BR method, a very noticeable differentiation is observed between the coverages associated with oil palm
cultivation and forest coverages. A notorious enhancement is also observed in linear objects such as access
roads, lot boundaries, and clear differentiation between grasslands and bare soil.
Finally, the result of PC the method, as well as the HFM method, presents a very high similarity
with the original image. No noticeable modifications are observed in the SAR image contribution regarding
the texture associated with the coverages, as it is possible to appreciate in the BR and GS methods. The
similarity was found in the results presented by Quan et al. [39], which showed a notorious difference in the
visualization of colors associated with the different land covers. In contrast, the GS method better preserves
the optical image’s original colors. In contrast, the PC method presented more significant alterations.
Figure 4 shows the histograms of values per band for each of the fusion methods evaluated. The
blue color represents the original values of the Sentinel-2 image, and the orange color represents the values of
the corresponding fusion method. In the case of BR method, a shift was evidenced due to the change in the
values of the resulting image, in bands B2, B3, B4, B11, and B12, there is a similar trend in the distribution
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of the data compared with those of the original bands. However, in the other bands, primarily significant
distortions compared to the histograms of the original bands. In all cases, an increase in the range of the data
was observed. In the histogram for the HFM method, it was observed that there is a very high relationship
between the values of the merged image compared to the distribution of the values of the original bands. This
implies that a very high similarity was achieved between the image created from the optical and radar fusion
process compared to the original image. In the histogram of GS method, the B2, B3, B4, B5, B11, and B12
bands presented a noticeable change in their distribution compared to the original bands. However, the data
range was not altered. For bands B6, B7, B8, B8A, and B9, some changes were observed when compared
with the values of the original image bands, but they were not very significant. As shown in the comparison
of the histograms for the PC method, B2, B3, B4, B5, B11, and B12 bands presented significant distortions.
In contrast, the other bands are highly like the original image. This may be because the one principal
component replaced by the SAR image showed a high correlation with bands B6, B7, B8, B8A, and B9.
Figure 4. Histogram of values per band for each of the fusion methods evaluated
Figure 5 shows the comparison of the different fusion methods implemented. Notable differences
were observed in the results of the image fusion methods between the optical image, which can be seen in
Figure 5(a) the SAR image, which can be seen in Figure 5(b), especially in the areas of natural forests and oil
palm crops due to differences in the backscatter values presented in the SAR image, improving the visual
quality of the definition of linear elements such as access roads and delimitation of lots within the
experimental station. This was evidenced in the BR method, which can be seen in Figure 5(c) which
presented good differentiation in both land covers and linear elements. The method that best discriminated
linear objects corresponds to the HFM method can be seen in Figure 5(d) due to the improvement provided
by the HP filter. In the GS method can be seen Figure 5(e) large differences were found between forest and
oil palm land cover, like that observed by the BR method. The CP method, can be seen in Figure 5(f),
presented the largest visual distortions of the image where cover differentiation was not as noticeable.
As demonstrated by Li et al. [43], the generation of a synthetic image containing the characteristics
of both optical and SAR information sources allows better visualization of the land cover associated with
water bodies and flat surfaces because of the specular interaction of the radar signal. In addition, as stated by
Sarzynski et al. [20], the linking of information captured by optical and SAR sensors offers a significant
advantage in the differentiation of coverages associated with areas of oil palm crops and forests, verifying the
results achieved in this study by the BR and GS fusion methods, where more significant differences between
these two coverages are observed.
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(a) (b) (c)
(d) (e) (f)
Figure 5. Comparison of images evaluated (a) optical image in true color (RGB) combination, (b) SAR image
in VH polarization, and (c) BR, (d) HFM, (e) GS, and (f) CP methods in true color (RGB) combination
3.2. Evaluation of optical/SAR image fusion methods
This section presents the results achieved with the statistical indicators: RMSE, SDT, PSNR, CC,
and ERGAS to evaluate the quality of the fusion methods. For all fusion methods, the RMSE values were
remarkably close to zero, indicating that the associated error in comparing the fused and original images is
very low. This is also the case with the SDT, which for all methods was close to zero, with a differential
increase in the case of BR method, especially between bands B7, B8, B9A, and B9. Regarding the evaluation
of the PSNR index, it was observed that the values for all the evaluated methods were high, which means that
a very high noise level was not found in the images compared to the original image. The method that
evidenced a higher amount of noise using the PSNR index was the BR method. The method that achieved the
highest CC was the HFM method, reaching values higher than 0.9 in all bands, except in bands B8A and B9,
which presented a value of 0.89 and 0.83, respectively. For the BR and PC methods, very high CC values
were reached in bands B2, B3, B4, B5, B11, and B12. However, the other bands achieved shallow correlation
values, which coincide with an increase in the error of the RMSE and PSNR indexes. These results are like
those presented by Quan et al. [39], where the average CC of the fused image bands by the GS method
presented higher values than the PC method in different study areas evaluated. In addition, he also observed
better results in both SDT and PSNR. The HFM method reached the best ERGAS index, followed by the GS
and PC methods, meaning that the fusion processes with SAR images did not present significant distortions.
In the case of the BR method, mean values were obtained in the result of ERGAS index. Table 1 shows the
results of the statistical indicators for the BR and HFM methods and Table 2 the results for the GS and PC
methods.
In the literature, it is possible to find some authors who point out the importance of implementing
statistical metrics to measure the quality of the results reached with fusion methods. In the results presented
by Kulkarni and Rege [38] a similarity was found between the CC values in the optical spectrum bands B3
and B4, with a significant decrease in the near-infrared spectrum bands B8 and B8A (<0.3). However, they
differ in the results for the shortwave infrared bands B11 and B12, where in this study, a high CC was
achieved (>0.9) in the HFM and BR methods. A similar result was also found in the ERGAS index, where
the PC method presented a better result than the BR method.
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Evaluation of optical and synthetic aperture radar image fusion methods … (Jose Manuel Monsalve-Tellez)
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Table 1. Result of the quality evaluation of the BR and HFM methods
Sentinel-2 Brovey (BR) High-frequency modulation (HFM)
bands RMSE SDT PSNR CC RMSE SDT PSNR CC
Band2 0.00013 0.03075 21.196 0.760 0.00000 0.01273 42.712 0.961
Band3 0.00019 0.04058 19.050 0.711 0.00001 0.01726 40.470 0.955
Band4 0.00015 0.07272 20.647 0.921 0.00001 0.02781 41.206 0.984
Band5 0.00030 0.06888 14.716 0.744 0.00001 0.02875 36.971 0.959
Band6 0.00073 0.14446 7.664 0.489 0.00002 0.03574 32.157 0.880
Band7 0.00090 0.20041 7.575 0.630 0.00002 0.04794 32.065 0.903
Band8 0.00089 0.20593 7.980 0.661 0.00002 0.05038 32.363 0.916
Band8A 0.00101 0.21603 7.424 0.586 0.00003 0.05033 32.143 0.889
Band9 0.00100 0.20225 9.337 0.493 0.00003 0.04219 32.260 0.833
Band11 0.00063 0.16429 9.095 0.802 0.00002 0.06406 35.099 0.966
Band12 0.00033 0.13907 13.980 0.914 0.00001 0.05067 36.656 0.983
ERGAS 0.3200 0.0098
Table 2. Result of the quality evaluation of the GS and PC methods
Sentinel-2 Gram-Schmidt (GS) Principal component (PC)
bands RMSE SDT PSNR CC RMSE SDT PSNR CC
Band2 0.00001 0.00971 31.727 0.664 0.00002 0.01248 28.311 0.144
Band3 0.00002 0.01295 28.561 0.546 0.00002 0.01704 27.492 0.283
Band4 0.00002 0.02140 25.576 0.681 0.00005 0.02781 22.845 0.014
Band5 0.00003 0.02140 24.145 0.522 0.00004 0.02895 23.548 0.171
Band6 0.00003 0.03386 25.429 0.640 0.00003 0.02688 26.098 0.612
Band7 0.00003 0.04684 26.152 0.790 0.00006 0.03765 22.340 0.265
Band8 0.00004 0.04973 25.843 0.780 0.00005 0.03835 22.377 0.378
Band8A 0.00004 0.04859 26.163 0.770 0.00005 0.03737 22.472 0.317
Band9 0.00002 0.03742 28.238 0.853 0.00005 0.03392 22.685 0.361
Band11 0.00006 0.04934 23.388 0.589 0.00010 0.06641 17.418 0.072
Band12 0.00004 0.03961 23.244 0.684 0.00009 0.05256 17.691 -0.081
ERGAS 0.0275 0.0488
4. CONCLUSION
The implemented multitemporal reduction of speckle noise demonstrated its enormous usefulness in
increasing the quality of SAR images, facilitating their interpretation, and expanding the great applications
submitted by SAR systems in various fields. The results presented by this method made it possible to
generate fused images of very high quality and without significant affectations caused by the speckle effect
of the SAR images. In this research, it was demonstrated that the information from Sentinel-2 optical sensors
and Sentinel-1 SAR are complementary and by applying image fusion methods it is possible to generate a
new synthetic image with both characteristics. In this way, it is possible to better understand the
characteristics present in a study area by enhancing the coverage’s geometric and texture/roughness
characteristics without losing the visualization of the color composition provided by the optical image. The
methods that visually presented the most characteristic features of the SAR image are the BR and GS
methods. Despite reaching the best statistical indicators, the HFM and PC methods did not show significant
visual changes regarding SAR image contribution. The high capacity of cloud computing platforms and the
high availability of information on issues related to earth observation data, such as GEE, demonstrates the
potential of using image fusion techniques in any area of the world where detailed analysis of the behavior of
the different coverages present in the study area is required. For future research, it is recommended to
implement optical and SAR image fusion methods applied to Sentinel imagery to assess land cover
classification accuracy. Since the fusion methods evaluated allow the creation of a synthetic image with all
the bands of the original image, in this case, eleven bands, are useful for classification algorithms.
ACKNOWLEDGEMENTS
Acknowledgments to the “Universidad Católica de Manizales” and the master’s program in remote
sensing for the support and guidance provided in the project’s development. Thanks to the “Centro de
Investigación en Palma de Aceite-CENIPALMA”, for the technical support and financing of this research.
Finally, thanks to the European Space Agency (ESA) for providing free optical and SAR data.
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BIOGRAPHIES OF AUTHORS
Jose Manuel Monsalve-Tellez is a topographic engineer from Universidad del
Valle and a master in remote sensing from Universidad Católica de Manizales, Colombia. He
serves as Geographic Information Systems and Remote Sensing researcher in the Geomatics
area, attached to the Agronomy program, of the Oil Palm Research Center, Cenipalma. Her
research interests include the application of remote sensing in precision agriculture, image
fusion process, and artificial intelligence. He can be contacted at jmonsalve@cenipalma.org.
Yeison Alberto Garcés-Gómez received a bachelor’s degree in Electronic
Engineering, and master's and Ph.D. degrees in Engineering from Electrical, Electronic and
Computer Engineering Department, Universidad Nacional de Colombia, Manizales, Colombia,
in 2009, 2011, and 2015, respectively. He is a full professor at the Academic Unit for Training
in Natural Sciences and Mathematics, Universidad Católica de Manizales, and teaches several
courses such as Experimental Design, Statistics, and Physics. His main research focus is on
applied technologies, embedded systems, power electronics, and power quality, but also many
other areas of electronics, signal processing, and didactics. He can be contacted at
ygarces@ucm.edu.co.
Jorge Luis Torres-León is a system and computer engineer from Universidad
Industrial de Santander. Master in Geoinformation Sciences and Earth Observation, with
emphasis in Geoinformatics from the University of Twente (Netherlands, ITC). In previous
years he served as GeoInformation and Technology Manager for the General Directorate of
Military Geographic Studies of the Ministry of Defense of Saudi Arabia. Today he serves as
Leader of the Geomatics area, a position he has held for the last 9 years. At this moment he
directs the lines of research in geographic information systems, remote sensing, and precision
agriculture, attached to the Agronomy program of the Research Directorate of Cenipalma.
Member of the International Society of Precision Agriculture (ISPA) and certified drone pilot
in Colombia. He can be contacted at jltorres@cenipalma.org.