MDFA-AconvNet: A Novel Multiscale Dilated Fusion Attention All-Convolution Network for SAR Target Classification

Synthetic aperture radar (SAR) features all-weather and all-day imaging capabilities, long-range detection, and high resolution, making it indispensable for battlefield reconnaissance, target detection, and guidance. In recent years, deep learning has emerged as a prominent approach for the classification of SAR image targets, owing to its hierarchical feature extraction, progressive refinement, and end-to-end learning capabilities. However, challenges such as the high cost of SAR data acquisition and the limited number of labeled samples often result in overfitting and poor model generalization. In addition, conventional layers typically operate with fixed receptive fields, making it difficult to simultaneously capture multiscale contextual information and dynamically focus on salient target features. To address these limitations, this paper proposes a novel architecture: the Multiscale Dilated Fusion Attention All-Convolution Network (MDFA-AconvNet). The model incorporates a multiscale dilated attention mechanism that significantly broadens the receptive field across varying target scales in SAR images without compromising spatial resolution, thereby enhancing multiscale feature extraction. Furthermore, by introducing both channel attention and spatial attention mechanisms, the model is able to selectively emphasize informative feature channels and spatial regions relevant to target recognition. These attention modules are seamlessly integrated into the All-Convolution Network (A-convNet) backbone, resulting in comprehensive performance improvements. Extensive experiments on the MSTAR dataset demonstrate that the proposed MDFA-AconvNet achieves a high classification accuracy of 99.38% in ten target classes, markedly outperforming the original A-convNet algorithm. These compelling results highlight the model’s robustness against target variations and its significant potential for practical deployment, paving the way for more efficient SAR image classification and recognition systems.