GRASS GIS has contained remote sensing tools for decades, catering to the needs of users since the first generations of Landsat satellites. In this talk we will present the current efforts of integrating modern data sources and modern approaches into GRASS GIS. Tools exist for pre-processing recent very high resolution images, object-based image analysis (OBIA), Lidar data handling, etc. At the same time, efforts have gone into ensuring the scalability of tools for huge data sets. The presentation will provide a short brief on the general state of GRASS GIS development, go on to an overview of the remote sensing tools available, to end with a use case on how to use GRASS GIS for time series processing in a high performance cluster/grid computing environment.
GRASS GIS has been existing for over 30 years by now and provides a very large and diverse set of state-of-the-art tools for the analysis of spatial data. Less known by many, remote sensing tools have been part of it almost from the beginning. GRASS GIS provides a series of imagery analysis tools for pre-processing (radiometric correction, cloud detection, pansharpening, etc), creating derived indices (vegetation indices, texture analysis, principal components, fourier transform, etc), classifying (management of training zones, different classifiers, validation tools), and producing other derived products such as evapotranspiration and energy balance models. Next to these tools for satellite images, other tools exist for the handling of aerial photography for creation of orthophotos, and for the import and analysis of Lidar data.
In addition to these tools, efforts have gone into integrating current state-of-the-art methods such as object-based image analysis and machine learning. A complete toolchain exists to segment images using different algorithms, to create superpixels, to collect statistics characterizing the resulting objects, and to apply machine learning algorithms for classification. New modules also include unsupervised segmentation parameter optimization and active learning. Options for pixel-based classification have also been enlarged to a host of machine learning algorithms.
A specific aspect of treating the rapidly increasing amounts of satellite data is the scalability of tools. Through its modular structure, GRASS GIS also allows to easily parallelize certain operations, thus opening the door to the use of cluster/grid computing environments.
- Markus Neteler
- Moritz Lennert
- Markus Metz
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