PolSAR Image Classification Framework With POA Align and Cyclic Channel Attention

The sensitivity and dependence of polarization and scattering on target orientation and radar incident angle pose a significant challenge in interpreting polarimetric synthetic aperture radar (PolSAR) images. Previous studies have emphasized the potential advantages of compensating for target orientation and acquiring diverse scattering information by rotating the polarimetric matrix. To fully exploit interpixel polarization information and achieve consistency in the targets' polarization orientation angle (POA), we propose a transformer-based framework that combines POA align with cyclic channel attention for PolSAR image classification. First, we select relevant features and introduce an implicit layer to facilitate unsupervised learning of POA, guiding the alignment of target orientation. Second, considering the inherent correlation between input POA channels and the necessity for more rational and efficient utilization of channel information, we devise a lightweight channel attention module with local cross-channel interaction and a cyclic padding strategy. Finally, this article is devoted to presenting a unified global modeling framework based on transformers. The primary objective is to overcome the limitations of local modeling capability in CNNs and comprehensively capture distinctions between polarization channels and intricate relationships among polarimetric information across pixels. Extensive experiments and analyses have demonstrated the robustness and effectiveness of the proposed method.