Multiscale Attention-Enhanced Complex-Valued Graph U-Net for PolSAR Image Classification

The powerful graph convolutional network (GCN) for polarimetric synthetic aperture radar (PolSAR) image classification generally relies on real-valued features, ignoring the phase information and thus limiting the modeling of complex-valued (CV) polarization characteristics. To address this issue, this paper proposes a novel multiscale attention-enhanced CV graph U-Net model, abbreviated as MAE-CV-GUNet, by embedding CV-GCN into a graph U-Net framework augmented with multiscale attention mechanisms. First, a CV-GCN is constructed based on the real-valued GCN, to effectively capture the intrinsic amplitude and phase information of the PolSAR data, along with the underlying correlations between them. This way can well lead to an improved feature representation for PolSAR images. Based on CV-GCN, a CV graph U-Net (CV-GUNet) architecture is constructed by integrating multiple CV-GCN components, aiming to extract multi-scale features and further enhance the ability to extract discriminative features in the complex domain. Then, a multiscale attention (MSA) mechanism is designed, enabling the proposed MAE-CV-GUNet to adaptively learn the importances of features at various scales, thereby dynamically fusing the multiscale information among them. The comparisons and ablation experiments on three PolSAR datasets show that MAE-CV-GUNet has excellent performance in PolSAR image classification.