New scheme for impervious surface area mapping from SAR images with auxiliary user-generated content

Abstract

This article presents a new scheme to extract impervious surface area from synthetic-aperture radar (SAR) images exploiting auxiliary user-generated content (UGC). The presented scheme includes the automatic generation of training samples based on the combination of UGC and SAR data, and SAR data preprocessing, leading to impervious surface area classification through a clustering-based one-class support vector machine approach. Two areas-namely, the cities of Beijing and Taipei, have been analyzed using the Sentinel-1 SAR data to test and validate the proposed methodology. Experimental results show that the presented scheme improves the automatic selection of impervious surface training samples. Moreover, this scheme achieves a comparable classification performance to traditional methods without requiring time-consuming training point manual extraction. Results in this study will help to promote the application of UGC for urban remote sensing data interpretation.