Orbital synthetic aperture radar (SAR) C-band data acquired by ERS-1/2 in vv-polarization and Radarsat in hh-polarization during the period from 1996 to 1999 were used to evaluate their combined information potential for classification of land cover in the arid environment of Kuwait. Individual SAR scenes were orthorectified using a digital elevation model (DEM) of Kuwait, radiometrically adjusted for incidence angle effects, and mosaics were generated for the whole country. The data were coregistered as multichannel composites and integrated with geographical information system (GIS) layers of roads, hydrology, soils and vegetation. An adaptive spatial filter was used to increase the number of effective independent looks prior to generation of feature vectors based on SAR backscatter power values. A total of 13 classes of the joint ERS-1/2 and Radarsat images were identified based on Bhattacharya distance and geospatial pattern. The C-band radar backscatter observed by ERS and Radarsat was found to be related to vegetation cover, surface roughness, percentage of coarse material in the surface layer and moisture conditions. These factors are not independent, but are known to be correlated. The complexity of these dependencies made unambiguous classification of surface material difficult when using C-band data alone. Nevertheless, class labels were assigned using a maximum likelihood supervised classification incorporating field measurements and ancillary data such as soil, and surface sediment maps. When used in a simple two-class classification (e.g. low vs. high vegetation cover fraction, or smooth vs. rough soils), the overall accuracy of the combined ERS and Radarsat data was between 70 and 80%. The generated dataset is amenable to several label definitions based on the requirements of the intended use.
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