Urban Area Analysis in Single-polarized SAR Images Based On Unsupervised Deep Learning

Urban mapping from remote sensing images is important for monitoring urbanization. In this paper, we propose an unsupervised learning algorithm for high-resolution single-polarized synthetic aperture radar (SAR) image to extract man-made targets for urban area analysis. The proposed method mainly focuses on the special physical characteristics of man-made targets that are different from natural areas. Without polarimetric information, we propose the sub-band scattering pattern based on time-frequency analysis to describe the physical properties of targets, and then design an end-to-end neural network to learn the latent features and potential clusters. The proposed method is evaluated on three different urban areas acquired at C-band by Sentinel-1 and Gaofen-3, and X-band by TerraSAR-X, respectively. The experiments present the visualized result of man-made targets extraction and analyze some specific targets to show the effectiveness of our proposed method.