Spectral Structure-Aware Initialization and Probability-Consistent Self-Training for Cross-Scene Hyperspectral Image Classification

Cross-scene classification of hyperspectral images (HSI) aims to classify target domain (TD) data using only labeled source domain (SD) data and unlabeled TD data during training. However, challenges such as spectral shifts across scenes and semantic discrepancies between domains significantly degrade classification performance. To address these issues, domain adaptation (DA) has gained increasing attention in the hyperspectral remote sensing community. This paper proposes a novel framework for cross-scene HSI classification, termed Data Structure-Aware Initialization and Probability-Consistent Self-Training (S2PST) framework. The framework employs batch nuclear-norm maximization to constrain the probability responses of TD outputs, implicitly aligning feature distributions between SD and TD. To enhance the model’s robustness and spectral feature representation ability, we introduce a spectral structure-aware initialization method that integrates the strengths of traditional machine learning and deep learning. Furthermore, to mitigate the model’s bias toward SD training data, we propose a self-supervised training strategy that dynamically incorporates pseudo-labeled TD samples into the training process by comparing the similarity of high-confidence samples in the probability space between SD and TD. Extensive experiments are conducted on the Houston, HyRANK, and Pavia datasets, and compared with several state-of-the-art DA methods. The experiment results demonstrate the effectiveness of the proposed framework. Our code will be available at https://github.com/liurongwhm.