Dual Information Purification for Lightweight SAR Object Detection

Synthetic aperture radar (SAR) object detection requires accurate identification and localization of targets at various scales within SAR images. However, background clutter and speckle noise can obscure key features and mislead the knowledge distillation process. To address these challenges, we introduce the Dual Information Purification Knowledge Distillation (DIPKD) method, which improves the performance of the student model through three key strategies: denoising, enrichment, and decoupling. First, our Selective Noise Suppression (SNS) technique reduces speckle noise in global features by minimizing misleading information from the teacher model. Second, the Knowledge Level Decoupling (KLD) module separates features into target and non-target knowledge, balancing feature mapping and reducing background noise to enhance the extraction of critical information for the student model. Finally, the Reverse Information Transfer (RIT) module refines intermediate features in the student model, compensating for the loss of detailed local information. Experimental results demonstrate that DIPKD significantly outperforms existing distillation techniques in SAR object detection, achieving 60.2% and 51.4% mAP scores on the SSDD and HRSID datasets, respectively. Additionally, the student model shows performance improvements of 1.3% and 2.9% over the teacher model, highlighting the effectiveness of the information purification approach.