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

How to fully utilize the rich but complex scattering characteristics in polarimetric synthetic aperture radar (PolSAR) data is still a challenge. In this article, a hierarchical scattering-spatial interaction transformer (HSSIT) for polarimetric synthetic aperture radar (SAR) image classification is proposed to effectively combine scattering and spatial characteristics of PolSAR data. The proposed HSSIT adopts a multistage hierarchical structure to extract discriminative features. Specifically, the spatial feature extraction branch (SFEB) is designed to improve the global information perception ability for spatial features, which combines the advantages of the CNN and Transformer to extract local features and capture context dependencies between pixels. A scatter-aware branch (SAB) based on a Transformer is proposed to model the 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.