Multilevel Attention Networks for Synthetic Aperture Radar Automatic Target Recognition

At present, the convolutional neural network (CNN) has been successfully applied in the field of synthetic aperture radar automatic target recognition (SAR-ATR) due to its strong learning ability and automatic hierarchical feature representation. However, the CNN-based methods are good at extracting the local structural features of the target, but are not sufficient in representing the long-range context information between the target and the scene, which restricts the further improvement of the performance of the current SAR-ATR system. To overcome these limitations, in this letter, we propose a novel model named multilevel attention networks (MANets) for SAR target recognition. MANets consider both the local structural features and long-range contextual information simultaneously to improve the representation ability of SAR targets. First, a CNN backbone with five convolutional layers is built to extract multilevel and multiscale convolutional features from the SAR target. Second, these CNN features are fed into a multilevel attention enhancement module (MAEM) to capture the long-range contextual information from spatial, channel, and cross-level attention (CLA) perspectives. Third, a multiscale attention fusion module (MAFM) is designed to fuse and aggregate the multilevel and multiscale features, further enhancing the representation capability of SAR images. Extensively experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate that the proposed MANets achieve state-of-the-art SAR target recognition performance both in the standard operating condition (99.75% at full data volume) and the extended operating condition (97.92% at full data volume) setups. The source code will be released at github.com/Crush0416/MANets.