A Benchmark High-Resolution GaoFen-3 SAR Dataset for Building Semantic Segmentation

Deep learning is increasingly popular in remote sensing communities and already successful in land cover classification and semantic segmentation. However, most studies are limited to the utilization of optical datasets. Despite few attempts applied to synthetic aperture radar (SAR) using deep learning, the huge potential, especially for the very high resolution (VHR) SAR, are still underexploited. Taking building segmentation as an example, the VHR SAR datasets are still missing to the best of our knowledge. A comparable baseline for SAR building segmentation does not exist, and which segmentation method is more suitable for SAR image is poorly understood. This article first provides a benchmark high-resolution (1 m) GaoFen-3 SAR datasets, which cover nine cities from seven countries, review the state-of-the-art semantic segmentation methods applied to SAR, and then summarize the potential operations to improve the performance. With these comprehensive assessments, we hope to provide the recommendation and roadmap for future SAR semantic segmentation.

Automated summary

Deep learning is popular in remote sensing, but there is still untapped potential for synthetic aperture radar (SAR) data, especially very high resolution (VHR) SAR. This article provides a benchmark dataset and reviews state-of-the-art methods to improve SAR semantic segmentation in the future.