A Multiscale Convolution SAR Image Target Recognition Method Based on Complex-Valued Neural Networks

Recent advances in deep learning have driven significant success in synthetic aperture radar (SAR) automatic target recognition (ATR), particularly through convolutional neural network (CNN)-based classification algorithms. However, SAR images possess distinctive physical scattering properties owing to their unique imaging mechanism. Many deep learning algorithms rely solely on amplitude information, ignoring phase information, which may result in the loss of information in the original complex-valued SAR image and suboptimal performance. To tackle these problems, this article introduces a SAR target recognition approach based on complex-valued operations, designated as Complex-valued Residual Mish activation and Convolution Block Attention Module (CBAM) net (CRMC-Net). The CRMC-Net effectively utilizes the amplitude and phase information in complex-valued SAR data. Specifically, first, the elements of CNN, including the input and output layers, the convolution layers, the activation functions, and the pooling layers, are extended to the complex-valued domain. Secondly, in order to further enhance the representation ability of the model, multiscale information of the target is extracted through different convolution kernel sizes. Finally, the network constructs many complex-valued operation blocks to enhance the robustness of the designed network, including the complex-valued residual block (CReLBlock), complex-valued Mish (CMish) activation function, and complex-valued convolution block attention module (CCBAM). The experimental results obtained from the Moving and Stationary Target Capture and Recognition dataset (MSTAR) and OpenSARShip2.0 (OPS 2.0) dataset demonstrate that the proposed network model outperforms the traditional real-valued models. It can further reduce the classification error and enhance performance.