Decoupled Multi-Teacher: Cross-Modal Learning Enhanced Object Detection in SAR Imagery

Object Detection in Synthetic Aperture Radar (SAR) images holds significant potential for diverse remote sensing applications. Nevertheless, the substantial cost associated with object-level annotation poses a formidable challenge in SAR object detection. While cross-modal learning offers a promising solution to address this annotation hurdle by using annotated optical images, existing methods falter in achieving satisfactory results due to the significant modal gap between optical and SAR. In this study, we introduce the Decoupled Multi-Teacher (DMT) framework, specifically tailored for object detection in SAR images. By strategically decoupling cross-modal learning from detector training, DMT adeptly mitigates challenges associated with co-training disparate modalities. Our experiments showcase the substantial improvements achieved by the proposed method in SAR image object detection.