Urban Land-cover Mapping with High-resolution Spaceborne SAR Data

Urban areas around the world are changing constantly and therefore it is necessary to update urban land cover maps regularly. Remote sensing techniques have been used to monitor changes and update land-use/land-cover information in urban areas for decades. Optical imaging systems have received most of the attention in urban studies. The development of SAR applications in urban monitoring has been accelerated with more and more advanced SAR systems operating in space. This research investigated object-based and rule-based classification methodologies for extracting urban land-cover information from high resolution SAR data. The study area is located in the north and northwest part of the Greater Toronto Area (GTA), Ontario, Canada, which has been undergoing rapid urban growth during the past decades. Five-date RADARSAT-1 fine-beam C-HH SAR images with a spatial resolution of 10 meters were acquired during May to August in 2002. Three-date RADARSAT-2 ultra-fine-beam C-HH SAR images with a spatial resolution of 3 meters were acquired during June to September in 2008. SAR images were pre-processed and then segmented using multi-resolution segmentation algorithm. Specific features such as geometric and texture features were selected and calculated for image objects derived from the segmentation of SAR images. Both neural network (NN) and support vector machines (SVM) were investigated for the supervised classification of image objects of RADARSAT-1 SAR images, while SVM was employed to classify image objects of RADARSAT-2 SAR images. Knowledge-based rules were developed and applied to resolve the confusion among some classes in the object-based classification results. The classification of both RADARSAT-1 and RADARSAT-2 SAR images yielded relatively high accuracies (over 80%). SVM classifier generated better result than NN classifier for the object-based supervised classification of RADARSAT-1 SAR images. Well-designed knowledge-based rules could increase the accuracies of some classes after the object-based supervised classification. The comparison of the classification results of RADARSAT-1 and RADARSAT-2 SAR images showed that SAR images with higher resolution could reveal more details, but might produce lower classification accuracies for certain land cover classes due to the increasing complexity of the images. Overall, the classification results indicate that the proposed object-based and rule-based approaches have potential for operational urban land cover mapping from high-resolution space borne SAR images.

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