Multisource Shadow-Based Fuzzy Set (MSFS) Approach for Impervious Surfaces Mapping from Optical and SAR Data

Urban impervious surfaces (UIS) indicate the environmental and socioeconomic influences of rapid urbanization. Synthetic aperture radar (SAR) reflects the scattering behaviors of different land covers while multispectral data demonstrate their physicochemical properties. Numerous studies reported that the incorporation of SAR and optical data supplement each other for better extracting UIS, nevertheless, the shadow and layover effects remain unclear, especially in very high-resolution observations. This study analyzed the shadow and layover influences from both optical and SAR data for fine resolution UIS estimation. Given the SAR shadow and layover distribution, we proposed a multisource shadow-based fuzzy set (MSFS) approach for fusing optical and SAR in optical shadow areas using decision fusion. SAR layovers showed effectiveness in UIS extraction. MSFS delivered 3% and 7% improvement in overall accuracy compared with SVM and RF using feature fusion respectively.