Synergistic Use of Optical and PolSAR Imagery for Urban Impervious Surface Estimation

Abstract

Impervious surface estimation has been widely conducted with medium-high resolution optical imagery. Challenges however, remain in aspects of spectral similarity of different objects, mixed pixel, and shadows of tall buildings or large tree crowns. In order to reduce these uncertainties, this study explores the synergistic use of optical and radar remote sensing datasets in highly populated urban areas. A comparative analysis is performed between the RADARSAT-2 full POLSAR image and the SPOT5 optical image. For both datasets, the C5.0 decision tree algorithm is applied to select features extracted to build a decision tree for urban impervious surface classification. It is found in this study that optical and POLSAR images possess different merits in delineating urban land surfaces. The POLSAR data is helpful for delineating bright impervious surfaces (e.g., buildings) and bare soils, which is often a difficult task for optical data. However, the confusion between dark impervious surfaces and bare soils becomes a severe problem due to similar surface scattering mechanisms. The intrinsic characteristics of radar scattering (e.g., layover and shadow effects) also result in high uncertainties in dense urban areas. These problems are leveraged when both optical and decomposed POLSAR features are considered. The results from this study indicate that the synergistic use of optical and POLSAR data could be an efficient approach to improving the estimation of urban impervious surfaces.