SlideShare ist ein Scribd-Unternehmen logo
1 von 36
Downloaden Sie, um offline zu lesen
Chapter 5
 In order to take advantage of and make good use of remote sensing
data, we must be able to extract meaningful information from the
imagery.
 This brings us to the topic of discussion of interpretation and
analysis.
 Interpretation and analysis of remote sensing imagery involves
the identification and/or measurement of various targets in an
image in order to extract useful information about them.
 Targets in remote sensing images may be any features or
object which can be observed in an image, and have the
following characteristics
 Targets may be a point, line or area.
 This means that they can have any form, from a bus in a parking lot
or plane on a runway, to a bridge or roadway to a large expanse of
water or a field.
 The target must be distinguishable; it must contrast with other
features around it in the image.
 Much interpretation and identification of targets in remote sensing imagery
is performed manually or visually, by a human interpreter.
 In many cases this is done using imagery displayed in a pictorial or
photograph-type format, independent of what type of sensor was used to
collect the data and how the data were collected.
 In this case we refer to the data as being in analog format.
 Remote sensing images can also be represented in a computer as arrays of
pixels, with each pixel corresponding to a digital number representing the
brightness level of that pixel in the image.
 Both analogue and digital imagery can be displayed as black and white
images or as color images.
 When remote sensing data are available in digital format, digital
processing and analysis may be performed using a computer.
 Digital processing may be used to enhance data as a prelude to visual
interpretation.
 Digital processing and analysis may also be carried out to automatically
identify targets and extract information completely without manual
intervention by a human interpreter.
 However, rarely is digital processing and analysis carried out as a complete
replacement for manual interpretation. Often it is done to supplement of
remote sensing for air photo interpretation.
Method Merit Demerit
Human (Image
Interpretation)
• Interpreter’s knowledge are
available
• Excellent in spatial
information extraction
• Time Consuming
• Individual difference
Computer (Image
processing)
• Short processing time
reproductively
• Extraction of physical
quantities or indices is
possible
• Human Knowledge is
unavailable
• Spatial information
extraction is poor.
 Each pixel is characterized for
a brief time by some single
value of radiation (e.g.,
reflectance) converted by the
photoelectric effect into
electrons and then to a number
(see illustration at right)
 The area coverage of the pixel
(that is, the ground cell area it
corresponds to) is determined
by instantaneous field of view
(IFOV) of the sensor system.
 Resolution is defined as a measure of the ability of an
optical system or other system to distinguish between
signals that are spatially near or spectrally similar.
• Spatial - the size of the field-of-view, e.g. 10 x 10 m.
• Spectral - the number and size of spectral regions the sensor
records data in, e.g. blue, green, red, near-infrared, thermal
infrared, microwave (radar).
• Temporal - how often the sensor acquires data, e.g. every
30 days.
• Radiometric - the sensitivity of detectors to small differences in
electromagnetic energy.
• Spatial - the size of the field-of-view, e.g. 10 x 10 m.
• Spectral - the number and size of spectral regions the sensor
records data in, e.g. blue, green, red, near-infrared, thermal
infrared, microwave (radar).
• Temporal - how often the sensor acquires data, e.g. every
30 days.
• Radiometric - the sensitivity of detectors to small differences in
electromagnetic energy.
GG RR NIRNIRBB
10 m10 m
10 m10 m
JanJan
1515
FebFeb
1515
 The fineness of detail visible in an image.
◦ (course) Low resolution – smallest features not discernable
◦ (fine) High resolution – small objects are discernable
 Factors affecting spatial resolution
◦ Atmosphere, haze, smoke, low light, particles or blurred sensor
systems
 Spatial resolution is ability to distinguish between two closely
spaced objects on an image.
 Spatial resolution depends on the field of view (FOV) , altitude and
viewing angle of a sensor.
 The detail discernible in an image is dependent on the spatial
resolution of the sensor and refers to the size of the smallest possible
feature that can be detected.
 Spatial resolution of passive sensors depends primarily on their
instantaneous field of view (IFOV).
 The IFOV is the angular cone of visibility of the sensor and determines
the area on the earth’s surface which is seen from a given altitude at
one particular moment of time.
 The IFOV is the angular cone of visibility of the sensor and determines
the area of visibility of the sensor and determines the area on the earth
surface.
 The IFOV may also be defined as the area on the ground, which viewed
by a single instrument from a given altitude at any given instant of
time.
 This area on the ground is called the resolution cell and determines a
sensor spatial resolution.
 The term spectral resolution refers to the width of
spectral bands that a satellite imaging system can
detect. Often satellite imaging systems are multi-
spectral meaning that they can detect in several discrete
bands, it is the width of these bands that spectral
resolution refers too. The narrower the bands, the
greater the spectral resolution.
 Spectral resolution describes the ability of a
sensor with to define fine wavelength intervals.
 The finer the spectral resolution , the narrower the
wavelength range for a particular channel or band.

 Resolution refers to the dimension spectral and number of
wavelength regions (or band) in the EM spectrum to which the
sensor is sensitive.
 Based on the spectral resolution the sensors fall into the
following groups: Broad-band, Narrow band, spectral and
hyper spectral sensors.
 It uses advanced multichannel sensors.
 Detect hundreds of very narrow spectral bands throughout the
visible near infrared and mid-infrared portions of the EM
spectrum.
 Their very high spectral resolution facilities fine
discrimination between different targets based on their
spectral response in each of the narrow bands.
SpectralSpectral
ResolutionResolution
SpectralSpectral
ResolutionResolution
 Temporal resolution is a measure of how often data are
obtained for the same area (i.e. how often an area can
be revisited)
 The temporal resolution varies from hours for some
systems to about 20 days to others.
 High temporal resolution: Daily or twice daily
June 1, 2006June 1, 2006June 1, 2006June 1, 2006 June 17, 2006June 17, 2006June 17, 2006June 17, 2006 July 3, 2006July 3, 2006July 3, 2006July 3, 2006
Remote Sensor Data AcquisitionRemote Sensor Data AcquisitionRemote Sensor Data AcquisitionRemote Sensor Data Acquisition
16 days16 days16 days16 days
 The radiometric resolution of an imaging system
describes its ability to discriminate very slight
difference in energy.
 Radiometric resolution is a measure of the sensitivity
of a sensor to differences in the intensity of the
radiation measured in sensor.
 Radiometric resolution is a measure of how many grey
levels are measured between pure black and pure white.
 The radiometric resolution measured in bit.
 E.g. 1 bit system (2^1=2) only two radiation levels and
2-bit system measures four levels etc.
8-bit8-bit
(0 - 255)(0 - 255)
8-bit8-bit
(0 - 255)(0 - 255)
9-bit9-bit
(0 - 511)(0 - 511)
9-bit9-bit
(0 - 511)(0 - 511)
0
0
0
7-bit7-bit
(0 - 127)(0 - 127)
7-bit7-bit
(0 - 127)(0 - 127)0
10-bit10-bit
(0 - 1023)(0 - 1023)
10-bit10-bit
(0 - 1023)(0 - 1023)
 Radiometric resolution, or radiometric sensitivity
refers to the number of digital levels used to express
the data collected by the sensor. In general, the greater
the number of levels, the greater the detail of
information.

 Suppose you have a digital
image which has a
radiometric resolution of 6
bits. What is the maximum
value of the digital number
which could be represented
in that image?
 The number of digital values possible in an
image is equal to the number two (2 - for
binary coding in a computer) raised to the
exponent of the number of bits in the image.
The number of values in a 6-bit image
would be equal to 26
= 2 x 2 x 2 x 2 x 2 x 2 =
64. Since the range of values displayed in a
digital image normally starts at zero (0), in
order to have 64 values, the maximum value
possible would be 63.
 The radiometric
resolution of an
imaging system
describes its ability to
discriminate very slight
differences in energy
The finer the
radiometric resolution
of a sensor, the more
sensitive it is to
detecting small
differences in reflected
or emitted energy.
 Analysis of remote sensing imagery involves the identification
of various targets in an image and those targets may be
environmental or artificial features which consists of points,
lines and areas.
 Targets may be defined in terms of the way they reflect or
emit radiation.
 This radiation is measured and recorded by a sensor and
ultimately is depicted as an image product such as an air photo
or a satellite image.
 Recognizing targets is the key to interpretation and
information extraction. Basic elements of image interpretation
are:
 Tone
 Shape
 Size
 Pattern
 Texture
 Shadow
 Site, Situation and Association
 It refers to the relative brightness or color of objects in
an image.
 Generally, tone is the fundamental element for
distinguishing between different targets or features.
 Variations in tone also allows the elements of shape,
texture, and pattern of objects to be distinguished.
 It refers to the general form, structure, outline of
individual objects.
 Shape can be very distinctive clue for interpretation
 Straight edge shape typically represents urban or
agricultural targets, while natural features such as
forest edge more irregular in shape, except where man
has created a road or clear cuts.
 Farm or crop land irrigated systems would appear as
circular shape.
 Size of objects in an image is a function of scale
 It is important to assess the size of a target relative to
other objects in a scene as well as the absolute size, to
aid in the interpretation of that target.
 A quick approximation of target size can direct
interpretation to an approximate result more quickly.
 For e.g. if an interpreter had to distinguish zones of
land use and had identified an area with a number of
buildings in it, large buildings such as factories or
warehouses would suggest commercial property,
whereas small buildings would indicate residential use.
 Pattern refers to the spatial arrangement of visibility
discernible (detectable) objects.
 Typically an orderly repetition of similar tones and
textures will produce a distinctive and ultimately
recognizable pattern.
 Orchards with evenly spaced trees and urban streets
with regularly spaced houses are good examples of
pattern.
 It refers to the arrangement and frequency of tonal
variations in particular areas of an image.
 Rough texture would consists of mottled tone where
gray levels change abruptly in small area, where as
smooth texture would have very little tonal variation.
 Smooth texture are most often the result of uniform,
even surfaces, such as field or grass lands.
 A target with a rough surface and irregular structure
such as forest canopy, results in a rough textured
appearance.
 Texture is one of most important element for
distinguishing features in RADAR imagery.
 It is also very helpful in interpretation as it may provide
an idea of profile and relative height of target which
may make identification easier.
 However, shadows can also reduce or eliminate
interpretation in their area of influence, since target
within shadow are much less detectable from their
surrounding.
 Shadow is also helpful for enhancing or identifying
topography and landform, particularly in RADAR
imagery.
 It takes into account the relationship between other
recognizable objects or features in proximity to the
target of interest.
 The identification of features that one would expect to
associate with other features may provide information
to facilitate identification.
 For example, commercial properties may be associated
with proximity to the major transportation routes,
where as residential areas would be associated with
schools, playgrounds, sport field. Other example a lake
is associated with boats, a marine and adjacent
recreational land.
 The criteria of image interpretation is as follows:
 Image reading is an element in the form image interpretation.
It corresponds to simple identification of objects using such
elements as shape, size, pattern, tone, texture, color, shadow
and other associated relationship.
 Image measurement is the extraction of physical quantities
such as length, height, location, density, temperature and so
on, by using reference data or calibration data.
 Image analysis is the understanding of the relationship
between interpreted information and the actual status or
phenomenon and to evaluate the situation.
 The image interpretation process is clearly shown in figure
below.
Figure: Image Interpretation process

Weitere ähnliche Inhalte

Was ist angesagt?

Remote Sensing Platforms and Sensors
Remote Sensing Platforms and SensorsRemote Sensing Platforms and Sensors
Remote Sensing Platforms and SensorsUday kumar Devalla
 
Sensors for remote sensing
Sensors for remote sensingSensors for remote sensing
Sensors for remote sensingMohsin Siddique
 
Components of gis
Components of gisComponents of gis
Components of gisPramoda Raj
 
Remote sensing - Sensors, Platforms and Satellite orbits
Remote sensing - Sensors, Platforms and Satellite orbitsRemote sensing - Sensors, Platforms and Satellite orbits
Remote sensing - Sensors, Platforms and Satellite orbitsAjay Singh Lodhi
 
Thermal remote sensing
Thermal remote sensing   Thermal remote sensing
Thermal remote sensing Rahat Malik
 
Interaction of EMR with atmosphere and earth surface
Interaction of EMR with atmosphere and earth surfaceInteraction of EMR with atmosphere and earth surface
Interaction of EMR with atmosphere and earth surfaceSumant Diwakar
 
Chapter 1 (Introduction to remote sensing)
Chapter 1 (Introduction to remote sensing)Chapter 1 (Introduction to remote sensing)
Chapter 1 (Introduction to remote sensing)Shankar Gangaju
 
Remote sensing and image interpretation
Remote sensing and image interpretationRemote sensing and image interpretation
Remote sensing and image interpretationMd. Nazir Hossain
 
Image classification, remote sensing, P K MANI
Image classification, remote sensing, P K MANIImage classification, remote sensing, P K MANI
Image classification, remote sensing, P K MANIP.K. Mani
 
Spectral reflectance curve of dead stressed vegetation
Spectral reflectance curve of dead stressed vegetationSpectral reflectance curve of dead stressed vegetation
Spectral reflectance curve of dead stressed vegetationJunaid Ijaz
 
. Atmospheric window and reflectance curve
. Atmospheric window and  reflectance curve. Atmospheric window and  reflectance curve
. Atmospheric window and reflectance curvemarutiChilame
 
ENERGY INTERACTIONS WITH EARTH SURFACE FEATURES
 ENERGY INTERACTIONS WITH EARTH SURFACE FEATURES  ENERGY INTERACTIONS WITH EARTH SURFACE FEATURES
ENERGY INTERACTIONS WITH EARTH SURFACE FEATURES diponnath
 
Geo referencing by Mashhood Arif
Geo referencing by Mashhood ArifGeo referencing by Mashhood Arif
Geo referencing by Mashhood ArifKU Leuven
 

Was ist angesagt? (20)

Optical remote sensing
Optical remote sensingOptical remote sensing
Optical remote sensing
 
Introduction to Remote Sensing
Introduction to Remote SensingIntroduction to Remote Sensing
Introduction to Remote Sensing
 
Remote sensing
Remote sensingRemote sensing
Remote sensing
 
Remote sensing
Remote sensingRemote sensing
Remote sensing
 
Digital Elevation Model (DEM)
Digital Elevation Model (DEM)Digital Elevation Model (DEM)
Digital Elevation Model (DEM)
 
Remote Sensing Platforms and Sensors
Remote Sensing Platforms and SensorsRemote Sensing Platforms and Sensors
Remote Sensing Platforms and Sensors
 
Sensors for remote sensing
Sensors for remote sensingSensors for remote sensing
Sensors for remote sensing
 
Components of gis
Components of gisComponents of gis
Components of gis
 
Remote sensing - Sensors, Platforms and Satellite orbits
Remote sensing - Sensors, Platforms and Satellite orbitsRemote sensing - Sensors, Platforms and Satellite orbits
Remote sensing - Sensors, Platforms and Satellite orbits
 
Thermal remote sensing
Thermal remote sensing   Thermal remote sensing
Thermal remote sensing
 
Interaction of EMR with atmosphere and earth surface
Interaction of EMR with atmosphere and earth surfaceInteraction of EMR with atmosphere and earth surface
Interaction of EMR with atmosphere and earth surface
 
Chapter 1 (Introduction to remote sensing)
Chapter 1 (Introduction to remote sensing)Chapter 1 (Introduction to remote sensing)
Chapter 1 (Introduction to remote sensing)
 
Remote sensing and image interpretation
Remote sensing and image interpretationRemote sensing and image interpretation
Remote sensing and image interpretation
 
Visual Interpretation
Visual InterpretationVisual Interpretation
Visual Interpretation
 
Image classification, remote sensing, P K MANI
Image classification, remote sensing, P K MANIImage classification, remote sensing, P K MANI
Image classification, remote sensing, P K MANI
 
Spectral reflectance curve of dead stressed vegetation
Spectral reflectance curve of dead stressed vegetationSpectral reflectance curve of dead stressed vegetation
Spectral reflectance curve of dead stressed vegetation
 
Coordinate systems
Coordinate systemsCoordinate systems
Coordinate systems
 
. Atmospheric window and reflectance curve
. Atmospheric window and  reflectance curve. Atmospheric window and  reflectance curve
. Atmospheric window and reflectance curve
 
ENERGY INTERACTIONS WITH EARTH SURFACE FEATURES
 ENERGY INTERACTIONS WITH EARTH SURFACE FEATURES  ENERGY INTERACTIONS WITH EARTH SURFACE FEATURES
ENERGY INTERACTIONS WITH EARTH SURFACE FEATURES
 
Geo referencing by Mashhood Arif
Geo referencing by Mashhood ArifGeo referencing by Mashhood Arif
Geo referencing by Mashhood Arif
 

Ähnlich wie Chapter 5: Remote sensing

spatial resolutionin remote sensing
 spatial resolutionin remote sensing spatial resolutionin remote sensing
spatial resolutionin remote sensingGhassan Hadi
 
Basics of remote sensing and GIS.pptx
Basics of remote sensing and GIS.pptxBasics of remote sensing and GIS.pptx
Basics of remote sensing and GIS.pptxFUCKAGAIN
 
Surveying ii ajith sir class4
Surveying ii ajith sir class4Surveying ii ajith sir class4
Surveying ii ajith sir class4SHAMJITH KM
 
RADIOMETRIC RESOLUTION.pptx
RADIOMETRIC RESOLUTION.pptxRADIOMETRIC RESOLUTION.pptx
RADIOMETRIC RESOLUTION.pptxKuki Boruah
 
Working of photogrammetry and remote sensing
Working of photogrammetry and remote sensingWorking of photogrammetry and remote sensing
Working of photogrammetry and remote sensingNI BT
 
MOD 5 SVIT NOTES VTU SYLLABUS 2018 SCHEME.pdf
MOD 5 SVIT NOTES VTU SYLLABUS 2018 SCHEME.pdfMOD 5 SVIT NOTES VTU SYLLABUS 2018 SCHEME.pdf
MOD 5 SVIT NOTES VTU SYLLABUS 2018 SCHEME.pdfBhuvanaN12
 
Sensors optimized for 3 d digitization
Sensors optimized for 3 d digitizationSensors optimized for 3 d digitization
Sensors optimized for 3 d digitizationBasavaraj Patted
 
Remote sensing
 Remote sensing Remote sensing
Remote sensingFidy Zegge
 
Resolution.pptx remote sensing resolution
Resolution.pptx remote sensing resolutionResolution.pptx remote sensing resolution
Resolution.pptx remote sensing resolutionaaravpatel29
 
Resolution and scanning system
Resolution and scanning systemResolution and scanning system
Resolution and scanning systemAglaia Connect
 
Components of Remote Sensing
Components of Remote SensingComponents of Remote Sensing
Components of Remote SensingAbby Varghese
 
Optical and infrared remote sensing
Optical and infrared remote sensingOptical and infrared remote sensing
Optical and infrared remote sensingNeha Sharma
 
Infrared image enhancement using wavelet transform
Infrared image enhancement using wavelet transformInfrared image enhancement using wavelet transform
Infrared image enhancement using wavelet transformAlexander Decker
 
Applying pixel values to digital images
Applying pixel values to digital imagesApplying pixel values to digital images
Applying pixel values to digital imagesCharles Flynt
 
RS - Presentation by AP2023.pptx
RS - Presentation by AP2023.pptxRS - Presentation by AP2023.pptx
RS - Presentation by AP2023.pptxDrAbhishekPandey9
 

Ähnlich wie Chapter 5: Remote sensing (20)

spatial resolutionin remote sensing
 spatial resolutionin remote sensing spatial resolutionin remote sensing
spatial resolutionin remote sensing
 
Basics of remote sensing and GIS.pptx
Basics of remote sensing and GIS.pptxBasics of remote sensing and GIS.pptx
Basics of remote sensing and GIS.pptx
 
Surveying ii ajith sir class4
Surveying ii ajith sir class4Surveying ii ajith sir class4
Surveying ii ajith sir class4
 
Remote sensing
Remote sensingRemote sensing
Remote sensing
 
RADIOMETRIC RESOLUTION.pptx
RADIOMETRIC RESOLUTION.pptxRADIOMETRIC RESOLUTION.pptx
RADIOMETRIC RESOLUTION.pptx
 
Working of photogrammetry and remote sensing
Working of photogrammetry and remote sensingWorking of photogrammetry and remote sensing
Working of photogrammetry and remote sensing
 
MOD 5 SVIT NOTES VTU SYLLABUS 2018 SCHEME.pdf
MOD 5 SVIT NOTES VTU SYLLABUS 2018 SCHEME.pdfMOD 5 SVIT NOTES VTU SYLLABUS 2018 SCHEME.pdf
MOD 5 SVIT NOTES VTU SYLLABUS 2018 SCHEME.pdf
 
Sensors optimized for 3 d digitization
Sensors optimized for 3 d digitizationSensors optimized for 3 d digitization
Sensors optimized for 3 d digitization
 
Remote sensing
 Remote sensing Remote sensing
Remote sensing
 
Resolution.pptx remote sensing resolution
Resolution.pptx remote sensing resolutionResolution.pptx remote sensing resolution
Resolution.pptx remote sensing resolution
 
Remote Sensin
Remote SensinRemote Sensin
Remote Sensin
 
Remote sensing
Remote sensingRemote sensing
Remote sensing
 
Resolution and scanning system
Resolution and scanning systemResolution and scanning system
Resolution and scanning system
 
Components of Remote Sensing
Components of Remote SensingComponents of Remote Sensing
Components of Remote Sensing
 
MAPPING REMOTE PLANTS THROUGH REMOTE SENSING TECHNOLOGY AND GIS
MAPPING REMOTE PLANTS THROUGH REMOTE SENSING TECHNOLOGY AND GISMAPPING REMOTE PLANTS THROUGH REMOTE SENSING TECHNOLOGY AND GIS
MAPPING REMOTE PLANTS THROUGH REMOTE SENSING TECHNOLOGY AND GIS
 
Optical and infrared remote sensing
Optical and infrared remote sensingOptical and infrared remote sensing
Optical and infrared remote sensing
 
Infrared image enhancement using wavelet transform
Infrared image enhancement using wavelet transformInfrared image enhancement using wavelet transform
Infrared image enhancement using wavelet transform
 
Applying pixel values to digital images
Applying pixel values to digital imagesApplying pixel values to digital images
Applying pixel values to digital images
 
RS - Presentation by AP2023.pptx
RS - Presentation by AP2023.pptxRS - Presentation by AP2023.pptx
RS - Presentation by AP2023.pptx
 
Basic remote sensing and gis
Basic remote sensing and gisBasic remote sensing and gis
Basic remote sensing and gis
 

Mehr von Shankar Gangaju (20)

Tutorial no. 8
Tutorial no. 8Tutorial no. 8
Tutorial no. 8
 
Tutorial no. 7
Tutorial no. 7Tutorial no. 7
Tutorial no. 7
 
Tutorial no. 6
Tutorial no. 6Tutorial no. 6
Tutorial no. 6
 
Tutorial no. 3(1)
Tutorial no. 3(1)Tutorial no. 3(1)
Tutorial no. 3(1)
 
Tutorial no. 5
Tutorial no. 5Tutorial no. 5
Tutorial no. 5
 
Tutorial no. 4
Tutorial no. 4Tutorial no. 4
Tutorial no. 4
 
Tutorial no. 2
Tutorial no. 2Tutorial no. 2
Tutorial no. 2
 
Tutorial no. 1.doc
Tutorial no. 1.docTutorial no. 1.doc
Tutorial no. 1.doc
 
What is a computer
What is a computerWhat is a computer
What is a computer
 
Pointer
PointerPointer
Pointer
 
Array
ArrayArray
Array
 
9.structure & union
9.structure & union9.structure & union
9.structure & union
 
6.array
6.array6.array
6.array
 
5.program structure
5.program structure5.program structure
5.program structure
 
4. function
4. function4. function
4. function
 
3. control statement
3. control statement3. control statement
3. control statement
 
2. operator
2. operator2. operator
2. operator
 
1. introduction to computer
1. introduction to computer1. introduction to computer
1. introduction to computer
 
Ads lab
Ads labAds lab
Ads lab
 
Electromagnetic formula
Electromagnetic formulaElectromagnetic formula
Electromagnetic formula
 

Kürzlich hochgeladen

Robotics Group 10 (Control Schemes) cse.pdf
Robotics Group 10  (Control Schemes) cse.pdfRobotics Group 10  (Control Schemes) cse.pdf
Robotics Group 10 (Control Schemes) cse.pdfsahilsajad201
 
Introduction of Object Oriented Programming Language using Java. .pptx
Introduction of Object Oriented Programming Language using Java. .pptxIntroduction of Object Oriented Programming Language using Java. .pptx
Introduction of Object Oriented Programming Language using Java. .pptxPoonam60376
 
Javier_Fernandez_CARS_workshop_presentation.pptx
Javier_Fernandez_CARS_workshop_presentation.pptxJavier_Fernandez_CARS_workshop_presentation.pptx
Javier_Fernandez_CARS_workshop_presentation.pptxJavier Fernández Muñoz
 
ADM100 Running Book for sap basis domain study
ADM100 Running Book for sap basis domain studyADM100 Running Book for sap basis domain study
ADM100 Running Book for sap basis domain studydhruvamdhruvil123
 
priority interrupt computer organization
priority interrupt computer organizationpriority interrupt computer organization
priority interrupt computer organizationchnrketan
 
Introduction to Artificial Intelligence: Intelligent Agents, State Space Sear...
Introduction to Artificial Intelligence: Intelligent Agents, State Space Sear...Introduction to Artificial Intelligence: Intelligent Agents, State Space Sear...
Introduction to Artificial Intelligence: Intelligent Agents, State Space Sear...shreenathji26
 
Novel 3D-Printed Soft Linear and Bending Actuators
Novel 3D-Printed Soft Linear and Bending ActuatorsNovel 3D-Printed Soft Linear and Bending Actuators
Novel 3D-Printed Soft Linear and Bending ActuatorsResearcher Researcher
 
Ergodomus - LOD 400 Production Drawings Exampes - Copy.pdf
Ergodomus - LOD 400 Production Drawings Exampes - Copy.pdfErgodomus - LOD 400 Production Drawings Exampes - Copy.pdf
Ergodomus - LOD 400 Production Drawings Exampes - Copy.pdfgestioneergodomus
 
Overview of IS 16700:2023 (by priyansh verma)
Overview of IS 16700:2023 (by priyansh verma)Overview of IS 16700:2023 (by priyansh verma)
Overview of IS 16700:2023 (by priyansh verma)Priyansh
 
March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...
March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...
March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...gerogepatton
 
Defining the Clouds for entriprises.pptx
Defining the Clouds for entriprises.pptxDefining the Clouds for entriprises.pptx
Defining the Clouds for entriprises.pptxAshwiniTodkar4
 
Prach: A Feature-Rich Platform Empowering the Autism Community
Prach: A Feature-Rich Platform Empowering the Autism CommunityPrach: A Feature-Rich Platform Empowering the Autism Community
Prach: A Feature-Rich Platform Empowering the Autism Communityprachaibot
 
Uk-NO1 Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Exp...
Uk-NO1 Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Exp...Uk-NO1 Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Exp...
Uk-NO1 Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Exp...Amil baba
 
Design and Analysis of Algorithms Lecture Notes
Design and Analysis of Algorithms Lecture NotesDesign and Analysis of Algorithms Lecture Notes
Design and Analysis of Algorithms Lecture NotesSreedhar Chowdam
 
70 POWER PLANT IAE V2500 technical training
70 POWER PLANT IAE V2500 technical training70 POWER PLANT IAE V2500 technical training
70 POWER PLANT IAE V2500 technical trainingGladiatorsKasper
 
Network Enhancements on BitVisor for BitVisor Summit 12
Network Enhancements on BitVisor for BitVisor Summit 12Network Enhancements on BitVisor for BitVisor Summit 12
Network Enhancements on BitVisor for BitVisor Summit 12cjchen22
 
Curve setting (Basic Mine Surveying)_MI10412MI.pptx
Curve setting (Basic Mine Surveying)_MI10412MI.pptxCurve setting (Basic Mine Surveying)_MI10412MI.pptx
Curve setting (Basic Mine Surveying)_MI10412MI.pptxRomil Mishra
 
Livre Implementing_Six_Sigma_and_Lean_A_prac([Ron_Basu]_).pdf
Livre Implementing_Six_Sigma_and_Lean_A_prac([Ron_Basu]_).pdfLivre Implementing_Six_Sigma_and_Lean_A_prac([Ron_Basu]_).pdf
Livre Implementing_Six_Sigma_and_Lean_A_prac([Ron_Basu]_).pdfsaad175691
 
Module-1-(Building Acoustics) Noise Control (Unit-3). pdf
Module-1-(Building Acoustics) Noise Control (Unit-3). pdfModule-1-(Building Acoustics) Noise Control (Unit-3). pdf
Module-1-(Building Acoustics) Noise Control (Unit-3). pdfManish Kumar
 

Kürzlich hochgeladen (20)

Robotics Group 10 (Control Schemes) cse.pdf
Robotics Group 10  (Control Schemes) cse.pdfRobotics Group 10  (Control Schemes) cse.pdf
Robotics Group 10 (Control Schemes) cse.pdf
 
Introduction of Object Oriented Programming Language using Java. .pptx
Introduction of Object Oriented Programming Language using Java. .pptxIntroduction of Object Oriented Programming Language using Java. .pptx
Introduction of Object Oriented Programming Language using Java. .pptx
 
Javier_Fernandez_CARS_workshop_presentation.pptx
Javier_Fernandez_CARS_workshop_presentation.pptxJavier_Fernandez_CARS_workshop_presentation.pptx
Javier_Fernandez_CARS_workshop_presentation.pptx
 
ADM100 Running Book for sap basis domain study
ADM100 Running Book for sap basis domain studyADM100 Running Book for sap basis domain study
ADM100 Running Book for sap basis domain study
 
priority interrupt computer organization
priority interrupt computer organizationpriority interrupt computer organization
priority interrupt computer organization
 
Introduction to Artificial Intelligence: Intelligent Agents, State Space Sear...
Introduction to Artificial Intelligence: Intelligent Agents, State Space Sear...Introduction to Artificial Intelligence: Intelligent Agents, State Space Sear...
Introduction to Artificial Intelligence: Intelligent Agents, State Space Sear...
 
Novel 3D-Printed Soft Linear and Bending Actuators
Novel 3D-Printed Soft Linear and Bending ActuatorsNovel 3D-Printed Soft Linear and Bending Actuators
Novel 3D-Printed Soft Linear and Bending Actuators
 
Ergodomus - LOD 400 Production Drawings Exampes - Copy.pdf
Ergodomus - LOD 400 Production Drawings Exampes - Copy.pdfErgodomus - LOD 400 Production Drawings Exampes - Copy.pdf
Ergodomus - LOD 400 Production Drawings Exampes - Copy.pdf
 
Overview of IS 16700:2023 (by priyansh verma)
Overview of IS 16700:2023 (by priyansh verma)Overview of IS 16700:2023 (by priyansh verma)
Overview of IS 16700:2023 (by priyansh verma)
 
March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...
March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...
March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...
 
Defining the Clouds for entriprises.pptx
Defining the Clouds for entriprises.pptxDefining the Clouds for entriprises.pptx
Defining the Clouds for entriprises.pptx
 
Prach: A Feature-Rich Platform Empowering the Autism Community
Prach: A Feature-Rich Platform Empowering the Autism CommunityPrach: A Feature-Rich Platform Empowering the Autism Community
Prach: A Feature-Rich Platform Empowering the Autism Community
 
Uk-NO1 Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Exp...
Uk-NO1 Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Exp...Uk-NO1 Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Exp...
Uk-NO1 Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Exp...
 
Versatile Engineering Construction Firms
Versatile Engineering Construction FirmsVersatile Engineering Construction Firms
Versatile Engineering Construction Firms
 
Design and Analysis of Algorithms Lecture Notes
Design and Analysis of Algorithms Lecture NotesDesign and Analysis of Algorithms Lecture Notes
Design and Analysis of Algorithms Lecture Notes
 
70 POWER PLANT IAE V2500 technical training
70 POWER PLANT IAE V2500 technical training70 POWER PLANT IAE V2500 technical training
70 POWER PLANT IAE V2500 technical training
 
Network Enhancements on BitVisor for BitVisor Summit 12
Network Enhancements on BitVisor for BitVisor Summit 12Network Enhancements on BitVisor for BitVisor Summit 12
Network Enhancements on BitVisor for BitVisor Summit 12
 
Curve setting (Basic Mine Surveying)_MI10412MI.pptx
Curve setting (Basic Mine Surveying)_MI10412MI.pptxCurve setting (Basic Mine Surveying)_MI10412MI.pptx
Curve setting (Basic Mine Surveying)_MI10412MI.pptx
 
Livre Implementing_Six_Sigma_and_Lean_A_prac([Ron_Basu]_).pdf
Livre Implementing_Six_Sigma_and_Lean_A_prac([Ron_Basu]_).pdfLivre Implementing_Six_Sigma_and_Lean_A_prac([Ron_Basu]_).pdf
Livre Implementing_Six_Sigma_and_Lean_A_prac([Ron_Basu]_).pdf
 
Module-1-(Building Acoustics) Noise Control (Unit-3). pdf
Module-1-(Building Acoustics) Noise Control (Unit-3). pdfModule-1-(Building Acoustics) Noise Control (Unit-3). pdf
Module-1-(Building Acoustics) Noise Control (Unit-3). pdf
 

Chapter 5: Remote sensing

  • 2.  In order to take advantage of and make good use of remote sensing data, we must be able to extract meaningful information from the imagery.  This brings us to the topic of discussion of interpretation and analysis.
  • 3.  Interpretation and analysis of remote sensing imagery involves the identification and/or measurement of various targets in an image in order to extract useful information about them.  Targets in remote sensing images may be any features or object which can be observed in an image, and have the following characteristics
  • 4.  Targets may be a point, line or area.  This means that they can have any form, from a bus in a parking lot or plane on a runway, to a bridge or roadway to a large expanse of water or a field.  The target must be distinguishable; it must contrast with other features around it in the image.
  • 5.  Much interpretation and identification of targets in remote sensing imagery is performed manually or visually, by a human interpreter.  In many cases this is done using imagery displayed in a pictorial or photograph-type format, independent of what type of sensor was used to collect the data and how the data were collected.  In this case we refer to the data as being in analog format.  Remote sensing images can also be represented in a computer as arrays of pixels, with each pixel corresponding to a digital number representing the brightness level of that pixel in the image.  Both analogue and digital imagery can be displayed as black and white images or as color images.  When remote sensing data are available in digital format, digital processing and analysis may be performed using a computer.
  • 6.  Digital processing may be used to enhance data as a prelude to visual interpretation.  Digital processing and analysis may also be carried out to automatically identify targets and extract information completely without manual intervention by a human interpreter.  However, rarely is digital processing and analysis carried out as a complete replacement for manual interpretation. Often it is done to supplement of remote sensing for air photo interpretation. Method Merit Demerit Human (Image Interpretation) • Interpreter’s knowledge are available • Excellent in spatial information extraction • Time Consuming • Individual difference Computer (Image processing) • Short processing time reproductively • Extraction of physical quantities or indices is possible • Human Knowledge is unavailable • Spatial information extraction is poor.
  • 7.  Each pixel is characterized for a brief time by some single value of radiation (e.g., reflectance) converted by the photoelectric effect into electrons and then to a number (see illustration at right)  The area coverage of the pixel (that is, the ground cell area it corresponds to) is determined by instantaneous field of view (IFOV) of the sensor system.
  • 8.  Resolution is defined as a measure of the ability of an optical system or other system to distinguish between signals that are spatially near or spectrally similar.
  • 9. • Spatial - the size of the field-of-view, e.g. 10 x 10 m. • Spectral - the number and size of spectral regions the sensor records data in, e.g. blue, green, red, near-infrared, thermal infrared, microwave (radar). • Temporal - how often the sensor acquires data, e.g. every 30 days. • Radiometric - the sensitivity of detectors to small differences in electromagnetic energy. • Spatial - the size of the field-of-view, e.g. 10 x 10 m. • Spectral - the number and size of spectral regions the sensor records data in, e.g. blue, green, red, near-infrared, thermal infrared, microwave (radar). • Temporal - how often the sensor acquires data, e.g. every 30 days. • Radiometric - the sensitivity of detectors to small differences in electromagnetic energy. GG RR NIRNIRBB 10 m10 m 10 m10 m JanJan 1515 FebFeb 1515
  • 10.  The fineness of detail visible in an image. ◦ (course) Low resolution – smallest features not discernable ◦ (fine) High resolution – small objects are discernable  Factors affecting spatial resolution ◦ Atmosphere, haze, smoke, low light, particles or blurred sensor systems  Spatial resolution is ability to distinguish between two closely spaced objects on an image.  Spatial resolution depends on the field of view (FOV) , altitude and viewing angle of a sensor.  The detail discernible in an image is dependent on the spatial resolution of the sensor and refers to the size of the smallest possible feature that can be detected.
  • 11.  Spatial resolution of passive sensors depends primarily on their instantaneous field of view (IFOV).  The IFOV is the angular cone of visibility of the sensor and determines the area on the earth’s surface which is seen from a given altitude at one particular moment of time.  The IFOV is the angular cone of visibility of the sensor and determines the area of visibility of the sensor and determines the area on the earth surface.  The IFOV may also be defined as the area on the ground, which viewed by a single instrument from a given altitude at any given instant of time.  This area on the ground is called the resolution cell and determines a sensor spatial resolution.
  • 12.
  • 13.  The term spectral resolution refers to the width of spectral bands that a satellite imaging system can detect. Often satellite imaging systems are multi- spectral meaning that they can detect in several discrete bands, it is the width of these bands that spectral resolution refers too. The narrower the bands, the greater the spectral resolution.
  • 14.  Spectral resolution describes the ability of a sensor with to define fine wavelength intervals.  The finer the spectral resolution , the narrower the wavelength range for a particular channel or band. 
  • 15.  Resolution refers to the dimension spectral and number of wavelength regions (or band) in the EM spectrum to which the sensor is sensitive.  Based on the spectral resolution the sensors fall into the following groups: Broad-band, Narrow band, spectral and hyper spectral sensors.  It uses advanced multichannel sensors.  Detect hundreds of very narrow spectral bands throughout the visible near infrared and mid-infrared portions of the EM spectrum.  Their very high spectral resolution facilities fine discrimination between different targets based on their spectral response in each of the narrow bands.
  • 17.  Temporal resolution is a measure of how often data are obtained for the same area (i.e. how often an area can be revisited)  The temporal resolution varies from hours for some systems to about 20 days to others.  High temporal resolution: Daily or twice daily
  • 18. June 1, 2006June 1, 2006June 1, 2006June 1, 2006 June 17, 2006June 17, 2006June 17, 2006June 17, 2006 July 3, 2006July 3, 2006July 3, 2006July 3, 2006 Remote Sensor Data AcquisitionRemote Sensor Data AcquisitionRemote Sensor Data AcquisitionRemote Sensor Data Acquisition 16 days16 days16 days16 days
  • 19.  The radiometric resolution of an imaging system describes its ability to discriminate very slight difference in energy.  Radiometric resolution is a measure of the sensitivity of a sensor to differences in the intensity of the radiation measured in sensor.  Radiometric resolution is a measure of how many grey levels are measured between pure black and pure white.  The radiometric resolution measured in bit.  E.g. 1 bit system (2^1=2) only two radiation levels and 2-bit system measures four levels etc.
  • 20. 8-bit8-bit (0 - 255)(0 - 255) 8-bit8-bit (0 - 255)(0 - 255) 9-bit9-bit (0 - 511)(0 - 511) 9-bit9-bit (0 - 511)(0 - 511) 0 0 0 7-bit7-bit (0 - 127)(0 - 127) 7-bit7-bit (0 - 127)(0 - 127)0 10-bit10-bit (0 - 1023)(0 - 1023) 10-bit10-bit (0 - 1023)(0 - 1023)
  • 21.  Radiometric resolution, or radiometric sensitivity refers to the number of digital levels used to express the data collected by the sensor. In general, the greater the number of levels, the greater the detail of information. 
  • 22.  Suppose you have a digital image which has a radiometric resolution of 6 bits. What is the maximum value of the digital number which could be represented in that image?
  • 23.  The number of digital values possible in an image is equal to the number two (2 - for binary coding in a computer) raised to the exponent of the number of bits in the image. The number of values in a 6-bit image would be equal to 26 = 2 x 2 x 2 x 2 x 2 x 2 = 64. Since the range of values displayed in a digital image normally starts at zero (0), in order to have 64 values, the maximum value possible would be 63.
  • 24.  The radiometric resolution of an imaging system describes its ability to discriminate very slight differences in energy The finer the radiometric resolution of a sensor, the more sensitive it is to detecting small differences in reflected or emitted energy.
  • 25.  Analysis of remote sensing imagery involves the identification of various targets in an image and those targets may be environmental or artificial features which consists of points, lines and areas.  Targets may be defined in terms of the way they reflect or emit radiation.  This radiation is measured and recorded by a sensor and ultimately is depicted as an image product such as an air photo or a satellite image.  Recognizing targets is the key to interpretation and information extraction. Basic elements of image interpretation are:
  • 26.  Tone  Shape  Size  Pattern  Texture  Shadow  Site, Situation and Association
  • 27.
  • 28.  It refers to the relative brightness or color of objects in an image.  Generally, tone is the fundamental element for distinguishing between different targets or features.  Variations in tone also allows the elements of shape, texture, and pattern of objects to be distinguished.
  • 29.  It refers to the general form, structure, outline of individual objects.  Shape can be very distinctive clue for interpretation  Straight edge shape typically represents urban or agricultural targets, while natural features such as forest edge more irregular in shape, except where man has created a road or clear cuts.  Farm or crop land irrigated systems would appear as circular shape.
  • 30.  Size of objects in an image is a function of scale  It is important to assess the size of a target relative to other objects in a scene as well as the absolute size, to aid in the interpretation of that target.  A quick approximation of target size can direct interpretation to an approximate result more quickly.  For e.g. if an interpreter had to distinguish zones of land use and had identified an area with a number of buildings in it, large buildings such as factories or warehouses would suggest commercial property, whereas small buildings would indicate residential use.
  • 31.  Pattern refers to the spatial arrangement of visibility discernible (detectable) objects.  Typically an orderly repetition of similar tones and textures will produce a distinctive and ultimately recognizable pattern.  Orchards with evenly spaced trees and urban streets with regularly spaced houses are good examples of pattern.
  • 32.  It refers to the arrangement and frequency of tonal variations in particular areas of an image.  Rough texture would consists of mottled tone where gray levels change abruptly in small area, where as smooth texture would have very little tonal variation.  Smooth texture are most often the result of uniform, even surfaces, such as field or grass lands.  A target with a rough surface and irregular structure such as forest canopy, results in a rough textured appearance.  Texture is one of most important element for distinguishing features in RADAR imagery.
  • 33.  It is also very helpful in interpretation as it may provide an idea of profile and relative height of target which may make identification easier.  However, shadows can also reduce or eliminate interpretation in their area of influence, since target within shadow are much less detectable from their surrounding.  Shadow is also helpful for enhancing or identifying topography and landform, particularly in RADAR imagery.
  • 34.  It takes into account the relationship between other recognizable objects or features in proximity to the target of interest.  The identification of features that one would expect to associate with other features may provide information to facilitate identification.  For example, commercial properties may be associated with proximity to the major transportation routes, where as residential areas would be associated with schools, playgrounds, sport field. Other example a lake is associated with boats, a marine and adjacent recreational land.
  • 35.  The criteria of image interpretation is as follows:  Image reading is an element in the form image interpretation. It corresponds to simple identification of objects using such elements as shape, size, pattern, tone, texture, color, shadow and other associated relationship.  Image measurement is the extraction of physical quantities such as length, height, location, density, temperature and so on, by using reference data or calibration data.  Image analysis is the understanding of the relationship between interpreted information and the actual status or phenomenon and to evaluate the situation.  The image interpretation process is clearly shown in figure below.