Prototype-Guided Cyclic Self-Training for Cross-Scene Hyperspectral Image Classification

In recent years, unsupervised domain adaptation (UDA) based on deep learning has been widely applied to address the spectral shift problem in cross-scene hyperspectral image classification (HSIC). However, most existing UDA methods focus solely on learning from source domain (SD) or target domain (TD) features, without fully exploiting the valuable class-discriminative information in the TD, resulting in limited performance on target data. To tackle this issue, we propose a prototype-guided cross-domain cyclic self-training (PGCST) framework. Specifically, we combine domain adversarial training with prototype-guided domain adaptation (PGDA) to align both global and class-wise distributions across domains. To better exploit TD information, we introduce a mutual information maximization (MIM) strategy to enhance the compactness and discriminability of target features. Furthermore, we propose a novel pseudo-label selection method that incorporates a classification loss-based cyclic self-training (CST) mechanism to improve the model’s discriminative ability on target samples. Experimental results on two cross-scene hyperspectral datasets demonstrate that the proposed method outperforms several state-of-the-art approaches.