Spectral Prototype Attention Domain Adaptation for Hyperspectral Image Classification
2025
Weina Zhang | Runshan Hu | Jierui Wang | Lanlan Zhang | Chenyang Zhu
Hyperspectral image (HSI) classification is often challenged by cross-scene domain shifts and limited target annotations. Existing approaches relying on class-agnostic moment matching or confidence-based pseudo-labeling tend to blur decision boundaries, propagate noise, and struggle with spectral overlap and class imbalance. We propose Spectral Prototype Attention Domain Adaptation (SPADA), a framework that integrates an attention-guided spectral&ndash:spatial backbone with dual prototype banks and distance-based posterior modeling. SPADA performs global and class-conditional alignment through source supervision, kernel-based distribution matching, and prototype coupling, followed by diversity-aware active adaptation and confidence-calibrated refinement via prior-adjusted self-training. Across multiple cross-scene benchmarks in urban and inter-city scenarios, SPADA consistently outperforms strong baselines in overall accuracy, average accuracy, and Cohen&rsquo:s &kappa:, achieving clear gains on classes affected by spectral overlap or imbalance and maintaining low variance across runs, demonstrating robust and stable domain transfer.
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