GHSS-Net: A Graph-enhanced Hybrid Self-supervised Framework for PolSAR Image Classification

Recently, deep neural networks have improved the performance of polarimetric synthetic aperture radar (PolSAR) image classification. Nonetheless, traditional approaches based on deep learning are highly dependent on substantial labeled datasets, which is difficult to obtain for PolSAR images. To tackle this issue, self-supervised learning is introduced to the studied task, exploiting hidden information within massive unlabeled data. Current self-supervised methods predominantly rely on a single paradigm, either generative or contrastive learning, yet each captures only partial features critical for PolSAR image classification. Therefore, to achieve comprehensive PolSAR image interpretation, we propose GHSS-Net, a graph-enhanced hybrid self-supervised framework for PolSAR image classification. Integrating graph-based generative learning and pixel-level contrastive learning through a dual-branch architecture, GHSS-Net improves classification results in scenarios with sparse annotations. Specifically, the generative learning branch utilizes superpixel-based graph neural networks with a low-rank constraint for representation learning by masking and reconstructing graphs. To compensate for the lack of fine-grained information, the contrastive learning branch captures pixel-level features through an implicit comparison process. We validate the effectiveness of GHSS-Net on four benchmark datasets. Experimental results substantiate that GHSS-Net attains state-of-the-art performance compared with other competitors with limited labels.