Synthetic aperture radar target recognition via deep attention convolutional network assisted by multiscale residual despeckling network

Owing to the inherent characteristics of synthetic aperture radar (SAR) imaging system based on the coherent imaging modality, SAR images are inevitably corrupted by speckle noise, thereby affecting SAR target recognition accuracy. We explore the impact of speckle noise on SAR target recognition performance. Subsequently, we propose a deep attention convolutional network assisted by a multiscale residual despeckling network to improve SAR target recognition accuracy under speckle corruption. Noise information is first learned via a despeckling subnetwork consisting of dual-branch multiscale feature extraction, feature fusion, and adaptive feature channel selection. Then a classification subnetwork with a cross-dimension interaction attention mechanism is designed to realize feature extraction and identity reasoning of SAR targets. Experimental results on the benchmark moving and stationary target acquisition and recognition dataset demonstrate the effectiveness and superiority of the proposed method.