SAR target recognition based on CNN with 2-D dual-tree complex wavelet transform decomposition

Synthetic aperture radar (SAR) is an effective imaging and observation sensor that has been widely applied in both military and civilian fields. Deep learning approaches have gained prominence in SAR target recognition and received extensive attention. However, these methods often struggle when data is scarce, leading to insufficient training and challenges in effective feature extraction. To address this limitation, we propose a less data-dependent feature extraction framework. Specifically, we introduce the dual-tree complex wavelet transform (DTCWT) to capture multi-frequency feature details of SAR images, integrated with convolutional neural network. This approach enables effective extraction of high- and low-frequency information. By leveraging the characteristics of these frequency features, low-frequency subbands are used to emphasize the global structural features in the images, and high-frequency subbands are employed to identify the significance of different regions in the images. In response to the aforementioned characteristics, we introduced an attention mechanism to effectively incorporate high-frequency local information into low-frequency global information, thereby enhancing feature representation and recognition efficiency. Moreover, we propose an adaptive rotational convolution, and apply it to the high-frequency feature extraction. The adaptive rotational convolution can adapt to the directionally selective subbands with a single convolution kernel. Experiments conducted on the MSTAR and SAR car datasets demonstrate that the proposed method can achieve better recognition performance with fewer parameters, especially on small-scale datasets. The ablation study also confirms the effectiveness of the introduced DTCWT and rotational convolution.