An SAR Target Classification Algorithm Based on the Central Coordinate Attention Module

Recent studies show that the attention mechanism can greatly improve the performance of synthetic aperture radar (SAR) target classification. However, the existing attention models, such as squeeze-and-excitation (SE), convolutional block attention module (CBAM), and coordinate attention (CA), emphasize the importance of the attention features of different channels, different columns, or different rows, ignoring the importance of pixel-level feature in the attention feature maps. Moreover, these models ignore the center-distribution characteristic of the targets in SAR images, so the feature importance difference between the central target and the interference at the image borders cannot be well distinguished. As a consequence, they cannot achieve a high classification accuracy. In order to solve the above problems, this article proposes an SAR target classification algorithm based on the central CA module (CCAM). CCAM highlights those important pixels in the feature maps through a pixel-level attention feature fusion strategy. Therefore, the importance difference of each pixel in the feature maps can be well distinguished. In addition, CCAM designs a center-importance weighting kernel, which highlights the center-distribution characteristic information of the SAR targets in the images and weakens the interference at the image borders. Therefore, the feature importance difference between the central target and the interference at the image borders can be well distinguished. Finally, in order to achieve high efficiency, the proposed CCAM is incorporated into the convolutional neural network (CNN) for final SAR target classification. Undoubtedly, CCAM can greatly elevate the target feature representation completeness, thus improving the SAR target classification accuracy with a high efficiency. Experimental results on the MSTAR dataset verify the superiority and effectiveness of the proposed CCAM.