Deep learning target detection network guided by physical mechanism

Synthetic aperture radar (SAR) images often face problems such as coherent speckle noise, low contrast and complex target geometric characteristics due to their unique coherent imaging mechanism, which limits the performance of traditional target detection methods. This paper proposes a target detection technology based on deep learning guided by the physical mechanism of synthetic aperture radar (SAR) imaging. By integrating SAR electromagnetic scattering characteristics and data-driven features, a highly interpretable and high-precision detection model is constructed to solve the problems of missed alarms, false alarms and insufficient multi-category recognition accuracy in traditional methods in SAR image processing. By introducing an anchor-free mechanism, a dynamic sample allocation strategy and a spatial topological structure construction method, the network can effectively extract the physical and semantic features of SAR images and improve the accuracy and interpretability of target detection. Experimental results show that this method significantly improves the detection performance in complex scenes, providing technical support for the application of SAR images in environmental monitoring, disaster assessment and other fields.