Target Extraction Based on Cross-Domain Alignment and Self-Correlation Mechanism With Weak-Labeled SAR Data

Sijia Ma, Libao Zhang

Abstract: Target extraction is a significant task in synthetic aperture radar (SAR) image processing. Recently, SAR target extraction with weak labels has attracted great attention due to the low labeling cost. However, weak-labeled SAR data brings great challenges to the data-driven methods: 1) the location and structural information of the targets are lost in weak labels and 2) discrepancy of heterogeneous SAR images restricts the training efficiency of the model. In this letter, a novel cross-domain self-correlation aware network (CSANet) for SAR target extraction based on image-level weak labels is proposed to address such challenges. First, the cross-domain representation alignment (CRA) strategy is proposed to learn transferrable knowledge from heterogeneous SAR datasets. Through cross-domain alignment, invariant feature space is constructed to bridge the heterogeneous SAR data and improve the generalization performance of the model. Then, we propose a self-correlation aware extraction module with image-level weak labels, which only indicate whether the images contain the targets or not. Self-correlation module (SCM) is designed to preserve the context dependency of SAR pixels and compensate for the gap between weak labels and dense prediction. Finally, affinity-guided optimization (AGO) is introduced to learn the inner-pixel affinity and refine the coarse extraction maps with clear boundaries. Comparison with state-of-the-arts and the ablation experiments demonstrate the efficiency of our method.