Enhancing Knowledge Transfer in Hyperspectral Image Classification via Cross-scene Knowledge Integration

• URL: http://arxiv.org/abs/2512.08989v1
• Date: Mon, 08 Dec 2025 02:18:01 GMT
• Title: Enhancing Knowledge Transfer in Hyperspectral Image Classification via Cross-scene Knowledge Integration
• Abstract summary: Cross-scene Knowledge Integration (CKI) is a framework that explicitly incorporates target-private knowledge during transfer.<n> CKI achieves state-of-the-art performance with strong stability across diverse cross-scene HSI scenarios.
• Score: 20.304972437980112
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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