Improving urban impervious surface extraction by synergizing hyperspectral and polarimetric radar data using sparse representation

Accurate extraction of urban impervious surface (UIS) is essential for urban planning and environmental monitoring. However, multispectral remote sensing data for UIS extraction suffers from the inter-class spectral confusions, e.g. UIS and bare soil, and intra-class variations of sub-class UIS. Hyperspectral and full/dual-polarization synthetic aperture radar (full/dual PolSAR) data provide opportunities for reducing such confusions and have potential for fine UIS mapping, i.e., roads, buildings, and grounds. In this study, we first investigated the hyperspectral data (Gaofen-5) capability to reduce the intra/inter-class misclassification in comparison with multispectral data (Landsat-8). Then, we explored contributions of synergistically using full and dual PolSAR (ALOS-2 and Sentinel-1) with hyperspectral and multispectral data using optical-SAR sparse representation classification (OSSRC). Results showed that both the hyperspectral and the SAR polarization features helped better delineation between UIS and bare soil, and sub-class UIS (roads and buildings). The relative contribution of PolSAR was higher in multispectral data than in hyperspectral data, with full PolSAR contributed significantly. The combined hyperspectral and full PolSAR data using OSSRC delivered the best result, with an overall accuracy higher than 90%. The results indicate the promising capability of synergizing hyperspectral and full/dual PolSAR data for improving UIS extraction from advanced satellite data.