解决跨场景高光谱图像分类中的知识迁移难题
Enhancing Knowledge Transfer in Hyperspectral Image Classification via Cross-scene Knowledge Integration
- 通过无领域依赖投影减少光谱差异
- 在无标签重叠下仍能有效迁移，准确率提升显著
- 适合跨传感器、跨场景的复杂分类任务
知识迁移在高光谱图像（HSI）分类中潜力巨大，但受制于传感器导致的光谱差异和异构场景间的语义不一致。现有方法受限于同质域或仅共现类别的异构场景设置，当标签空间无重叠时，需源域完全覆盖目标域，忽略目标私有信息。为此，我们提出跨场景知识融合（CKI）框架，显式融入目标私有知识：(1) 光谱特征对齐（ASC），通过无领域依赖投影降低光谱差异；(2) 跨场景知识共享偏好（CKSP），利用源相似性机制（SSM）缓解语义不匹配；(3) 补充信息融合（CII），最大化利用目标特有互补线索。大量实验表明，CKI在多种跨场景HSI场景中达到领先性能且稳定性强。
原文摘要 · Abstract (English)
Knowledge transfer has strong potential to improve hyperspectral image (HSI) classification, yet two inherent challenges fundamentally restrict effective cross-domain transfer: spectral variations caused by different sensors and semantic inconsistencies across heterogeneous scenes. Existing methods are limited by transfer settings that assume homogeneous domains or heterogeneous scenarios with only co-occurring categories. When label spaces do not overlap, they further rely on complete source-domain coverage and therefore overlook critical target-private information. To overcome these limitations and enable knowledge transfer in fully heterogeneous settings, we propose Cross-scene Knowledge Integration (CKI), a framework that explicitly incorporates target-private knowledge during transfer. CKI includes: (1) Alignment of Spectral Characteristics (ASC) to reduce spectral discrepancies through domain-agnostic projection; (2) Cross-scene Knowledge Sharing Preference (CKSP), which resolves semantic mismatch via a Source Similarity Mechanism (SSM); and (3) Complementary Information Integration (CII) to maximize the use of target-specific complementary cues. Extensive experiments verify that CKI achieves state-of-the-art performance with strong stability across diverse cross-scene HSI scenarios.
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