EMC2A-Net: An Efficient Multibranch Crosschannel Attention Network for SAR Target Classification

In recent years, convolutional neural networks (CNNs) have demonstrated significant potential for synthetic aperture radar (SAR) target recognition. SAR images possess a strong sense of granularity and contain texture features of varying scales, including speckle noise, dominant scatterers and target contours, which are not typically considered in traditional CNN models. This paper proposes two residual blocks, termed EMC 2 A blocks, with multiscale receptive fields (RFs) based on a multibranch structure and designs an efficient isotopic architecture deep CNN (DCNN) called EMC 2 A-Net, whose structure is interpretable from a probability and mathematical statistics perspective. EMC 2 A blocks employ parallel dilated convolution with different dilation rates to effectively capture multiscale contextual features without significantly increasing the computational load. To further enhance the efficiency of multiscale feature fusion, this paper presented a multiscale feature cross-channel attention module, known as the EMC 2 A module, which adopts a local multiscale feature interaction strategy without dimensionality reduction. This strategy adaptively adjusts the weights of each channel using efficient one-dimensional (1D)-circular convolution and sigmoid function to guide attention at the global channel-wise level. Comparative results on the MSTAR dataset demonstrate that EMC 2 A-Net outperforms the other available models of the same type and possesses a relatively lightweight network structure. The ablation experiment results further demonstrate that the EMC 2 A module significantly enhances the model's performance by utilizing only a few parameters and appropriate cross-channel interactions.