1 research outputs found
CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images
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 owing to the complexity of SAR data. This paper
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 GIS data as complementary information and propose a novel
conditional GIS-aware network (CG-Net). The proposed model learns multi-level
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 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 datasets. The segmentation
results can be applied to reconstruct 3D building models at level-of-detail
(LoD) 1, which is demonstrated in our experiments