Spaceborne SAR Data for Regional Urban Mapping Using a Robust Building Extractor

Abstract

With the rapid development of urbanization, timely and accurate information on the spatial distribution of urban areas is essential for urban planning, environmental protection and sustainable urban development. To date, the main problem of urban mapping using synthetic aperture radar (SAR) data are that nonbuilding objects with high backscattering cause high false alarms, while small-scale buildings with low backscattering result in omission errors. In this paper, a robust building-area extraction extractor is proposed to solve the above problems. The specific work includes (1) building a multiscale and multicategory building area dataset to learn enough building features in various areas; (2) designing a multiscale extraction network based on the residual convolutional block (ResNet50) and a pyramid-based pooling module to extract more discriminative features of building areas and introducing the focal loss item as the object function of the network to further extract the small-scale building areas and (3) eliminating the false alarms using the Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI) index. GF-3 SAR data with a 10-m resolution of four regions in China are used to validate our method, and the regional building-area mapping results with overall accuracy above 85% and kappa coefficient not less than 0.73 are obtained. Compared with the current popular networks and the global human settlement layer (GHSL) product, our method shows better extraction results and higher accuracy in multiscale building areas. The experiments using Sentinel-1 and ALOS-2/PALSAR-2 data show that the proposed method has good robustness with different SAR data sources.