RESIDENTIAL EXTRACTION BASED ON WEAKLY-SUPERVISED SIMILARITY-AWARE MULTI-SOURCE ALIGNMENT STRATEGY WITH LIMITED SAR DATA

Residential extraction based on deep learning approach is a significant task in Synthetic Aperture Radar (SAR) image processing. However, extremely limited SAR data brings great challenges to the data-driven method: 1) pixel-wise annotations are hard to obtain due to the expensive cost; 2) There is not any universal large-scale SAR image dataset with heterogeneous SAR images. In this paper, a novel residential extraction method based on similarity-aware multi-source alignment strategy is proposed to solve such problems. Firstly, we propose a weakly-supervised Multi-source Similarity-aware Extraction Network (MSENet) to preserve the context dependency of pixels and improve the integrity of the extraction. Then, to tackle with the lack of training samples, a multi-source knowledge alignment strategy is proposed to learn transferrable knowledge from heterogeneous SAR datasets. Finally, affinity-guided optimization is introduced to refine the coarse maps with clear boundaries. Comprehensive experiments demonstrate the efficiency of our method.