Leveraging Physics-Guided Features for Domain Adaptation in SAR Target Classification

Semi-supervised domain adaptation offers a solution to the issues of data scarcity and annotation difficulty in Synthetic Aperture Radar (SAR) images by transferring knowledge from an existing, labeled source domain dataset to a minimally labeled target domain. However, current methods struggle with unique classes exclusive to the target domain and the inherent sampling bias towards specific acquisition angles present in the limited labeled target data, leading to learning an inadequate representation of the comprehensive target domain. This paper introduces a unified framework that leverages the class- and domain-independent SAR imaging physics to perform feature transformation across varying acquisition angles. The feature transformation module augments the sparsely labeled target data, thereby contributing to a more complete representation of the full target domain. Experimental results show that our proposed method outperforms existing techniques, highlighting the effectiveness of physics-guided feature extraction in bridging domain and class discrepancies amidst SAR images' angle sensitivity.