A Multi-Modality Feature Enhancement Method Based On Feature Disentanglement For Sar Image Target Detection

Synthetic Aperture Radar (SAR) ship detection algorithms have achieved extensive development in recent years. In spite of this, the insufficient data and the non-intuitive feature of SAR images still brought certain challenges. This paper proposes a multi-modality feature enhancement (MMFE) method based on feature disentanglement for SAR image target detection. By precisely exploring modality-shared features of optical and SAR images, MMFE can optimize the SAR feature representation capability. First, we propose a feature disentanglement (FD) module to acquire transferable modality-shared knowledge, thereby effectively alleviating the modality shift phenomenon in the subsequent modality alignment. Second, we introduce a multi-granularity modality alignment (MGMA) module that further eliminates inter-modality differences, ultimately achieving effective compensation for the SAR modality. Extensive experimental results convincingly demonstrate the compelling ability of MMFE.