SARDet-CL: Self-Supervised Contrastive Learning With Feature Enhancement and Imaging Mechanism Constraints for SAR Target Detection

In recent years, supervised learning has seen notable progress in research on aircraft target detection using synthetic aperture radar (SAR) images, but its performance is heavily restricted by the amount of labeled data. Self-supervised learning (SSL) can address this problem by pretraining on unlabeled data to extract generalizable features. However, existing SSL methods are predominantly developed based on approaches designed for natural image processing, making them unsuitable for fully understanding SAR images. Therefore, this study proposes a self-supervised contrastive learning (SARDet-CL) method that integrates feature enhancement and imaging mechanism constraints, tailored to the characteristics and requirements of aircraft target detection tasks. Specifically, SARDet-CL enhances the model’s ability to represent spatiotemporal features through spatial feature masking and multitemporal consistency learning, and robustly extracts target features using a dynamic threshold quantization strategy. In addition, structural information is inferred based on the SAR imaging mechanism and incorporated as an auxiliary self-supervisory signal to guide the model toward a more accurate understanding of SAR images. Experimental results based on various SAR datasets show that the proposed method outperforms other pretraining techniques in downstream SAR target detection tasks.