SAR Data Classification of Urban Areas by Means of Segmentation Techniques and Ancillary Optical Data

This is the authors' abstract. We don't add key points for this paper.

The classification of urban areas in terms of land-use/land-cover (LULC) maps is a challenging as well as essential task in order to monitor how the urban sprawl is changing the environment. This paper is devoted to the description of a novel procedure designed to exploit coarse-resolution SAR images and obtain both the built-up area extents and a LULC map of the individuated urban area. The approach starts from the previously developed BuiltArea algorithm to produce the built-up area extent map, exploiting the spatial correlation among neighboring pixels by means of local indicators of spatial association and gray level co-occurrence matrix (GLCM) features. After discriminating between urban and nonurban areas, a novel approach is presented that exploits segmentation techniques, spatial feature selection, and a supervised classifier to generate urban LULC maps. A robust chain, considering SAR data and using ancillary optical data is proposed and validated using data sets available in two test cases, the megacities of Shanghai and Beijing.