Topology-Aware Neighborhood Learning for Source-Free Cross-Scene Hyperspectral Image Classification
This work addresses the challenge of source-data-unavailable cross-scenario hyperspectral image classification by proposing a topology-aware unsupervised domain adaptation framework. The method uniquely integrates global collaborative representation with local nearest-neighbor search to construct a contextual neighborhood topology that comprehensively captures the intrinsic manifold structure of the target domain. To enhance pseudo-label quality, it introduces entropy-momentum-based pseudo-label refinement, complemented by a log-inner-product topological consistency constraint and an information maximization regularizer. Extensive experiments on three standard cross-scenario hyperspectral datasets demonstrate that the proposed approach significantly outperforms current state-of-the-art methods. Ablation studies further validate the effectiveness of each component, underscoring the critical role of topological modeling in source-free hyperspectral domain adaptation.