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Efficient and Lightweight Target Recognition for High Resolution Spaceborne SAR Images

Fast and reliable target recognition of the synthetic aperture radar (SAR) images has been widely used in the fields of the marine monitoring, military reconnaissance and strike all over the world. However, due to the difficulty of the intra-class difference and inter-class similarity of the multiclass targets in the high resolution SAR images, the existing methods are difficult to recognize the targets accurately when facing the spaceborne platforms with the high resource constraints. Therefore, in order to solve the above problems, we propose a novel recognition method based on the convolutional neural network (CNN). Firstly, we propose a lightweight CNN framework which regards densely connected convolutional network (DenseNet) as the baseline. Secondly, we advocate a strong discriminative loss function which efficiently improves the recognition accuracy of the targets in the spaceborne SAR images. Experiments are conducted on the TerraSAR dataset and MSTAR dataset to evaluate the proposed method. The results show that our method performs better than the baseline on the both benchmark datasets.