CV-SAR-Det: Target Detection for SAR Images via Deep Complex-Valued Network

In recent years, deep learning (DL) networks have achieved considerable success in the SAR target detection task. Here, current DL-based methods, originally designed for natural images, SAR images usually have been treated as real-valued images and solely utilize the magnitude information for feature extraction. However, different from natural images, SAR images are complex-valued data. The conversion from the complex-valued images to the real-valued images potentially leads to disregarding valuable target features inherent in original SAR data. To develop the complex-valued information inherent in SAR images adequately, a target detection method for SAR images using the complex-valued network (CV-SAR-Det) is proposed in this paper. Specifically, the CV-SAR-Det mainly includes three complex-valued elements: network structure, loss function, and data augmentation. Firstly, the network adopts a fully complex-valued structure for target detection, which effectively leverages the separability characteristics of targets inherent in the complex-valued SAR image. Secondly, a complex-valued loss function (CV-Loss) is designed for the optimization of the CV-SAR-Det, which highlights the importance of the complex-valued information. Finally, to further improve the detection performance, a complex-valued data augmentation method is proposed for diversifying the training data, which also improves the robustness of the network. The advantages of the CV-SAR-Det have been demonstrated by the experimental results based on the MiniSAR-KU/KA datasets, which are superior to the latest SAR target detection methods.