Polarimetric SAR Image Classification Based on Hierarchical Scattering-Spatial Interaction Transformer

How to fully utilize the rich but complex scattering characteristics in PolSAR data is still a challenge. In this paper, a hierarchical scattering-spatial interaction Transformer (HSSIT) for polarimetric SAR image classification is proposed to effectively combine scattering and spatial characteristics of PolSAR data. The proposed HSSIT adopts a multi-stage hierarchical structure to extract discriminative features. Specifically, spatial feature extraction branch (SFEB) is designed to improve the global information perception ability for spatial features, which combines the advantages of CNN and Transformer to extract local features and capture context dependencies between pixels. A scatter-aware branch (SAB) based on Transformer is proposed to model correlation between polarimetric scattering features. Furthermore, we further propose a cross attention based information exchange module, which aggregates the tokens from two branches to enhance the discrimination of features for land cover classification. Sufficient experiments are carried out on three widely used PolSAR datasets to certify the effectiveness and superiority of our proposed method.