CG-Net: Conditional GIS-Aware Network for Individual Building Segmentation in VHR SAR Images

Abstract

Object retrieval and reconstruction from very-high-resolution (VHR) synthetic aperture radar (SAR) images are of great importance for urban SAR applications, yet highly challenging due to the complexity of SAR data. This article addresses the issue of individual building segmentation from a single VHR SAR image in large-scale urban areas. To achieve this, we introduce building footprints from geographic information system (GIS) data as a complementary information and propose a novel conditional GIS-aware network (CG-Net). The proposed model learns multilevel visual features and employs building footprints to normalize the features for predicting building masks in the SAR image. We validate our method using a high-resolution spotlight TerraSAR-X image collected over Berlin. Experimental results show that the proposed CG-Net effectively brings improvements with variant backbones. We further compare two representations of building footprints, namely, complete building footprints and sensor-visible footprint segments, for our task, and conclude that the use of the former leads to better segmentation results. Moreover, we investigate the impact of inaccurate GIS data on our CG-Net, and this study shows that CG-Net is robust against positioning errors in the GIS data. In addition, we propose an approach of ground truth generation of buildings from an accurate digital elevation model (DEM), which can be used to generate large-scale SAR image data sets. The segmentation results can be applied to reconstruct 3-D building models at level-of-detail (LoD) 1, which is demonstrated in our experiments.