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

TL;DR

This paper introduces CKI, a novel framework that enhances hyperspectral image classification by effectively transferring knowledge across heterogeneous scenes with spectral and semantic differences.

Contribution

It proposes a comprehensive approach combining spectral alignment, semantic matching, and target-specific information integration for cross-scene HSI classification.

Findings

Achieves state-of-the-art performance in diverse scenarios

Demonstrates strong stability across different cross-scene settings

Effectively incorporates target-private knowledge during transfer

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.