LW-CMDANet: a novel attention network for SAR automatic target recognition

Deep-learning-based synthetic aperture radar automatic target recognition (SAR-ATR) plays a significant role in the military and civilian fields. However, data limitation and large computational cost are still severe challenges in the actual application of SAR-ATR. To improve the performance of the convolutional neural network (CNN) model with limited data samples in SAR-ATR, this article proposes a novel multidomain feature subspace fusion representation learning method, i.e., a lightweight cascaded multidomain attention network, namely, LW-CMDANet. First, we design a four-layer CNN model to perform hierarchical feature representation learning via the hinge loss function, which can efficiently alleviate the overfitting problem of the CNN model by a nongreedy training style with a small dataset. Then, a cascaded multidomain attention module, based on discrete cosine transform and discrete wavelet transform, is embedded into the previous CNN to further complete the class-specific feature extraction from both the frequency and wavelet transform domains of the input feature maps. Thus, the multidomain attention can enhance the feature extraction ability of previous nongreedy learning manner, to effectively improve the recognition accuracy of the CNN model. Experimental results on small SAR datasets show that our proposed method can achieve better or competitive performance than that of many current existing state-of-the-art methods in terms of recognition accuracy and computational cost.