Agreement Disagreement Guided Knowledge Transfer for Cross-Scene Hyperspectral Imaging

TL;DR

This paper introduces ADGKT, a novel framework that enhances cross-scene hyperspectral imaging by addressing gradient conflicts and capturing diverse target features through agreement and disagreement mechanisms.

Contribution

The proposed ADGKT framework uniquely combines agreement and disagreement strategies to improve knowledge transfer in cross-scene hyperspectral imaging, addressing gradient conflicts and feature diversity.

Findings

Outperforms existing methods in cross-scene HSI tasks

Effectively mitigates gradient conflicts during training

Captures diverse target scene features

Abstract

Knowledge transfer plays a crucial role in cross-scene hyperspectral imaging (HSI). However, existing studies often overlook the challenges of gradient conflicts and dominant gradients that arise during the optimization of shared parameters. Moreover, many current approaches fail to simultaneously capture both agreement and disagreement information, relying only on a limited shared subset of target features and consequently missing the rich, diverse patterns present in the target scene. To address these issues, we propose an Agreement Disagreement Guided Knowledge Transfer (ADGKT) framework that integrates both mechanisms to enhance cross-scene transfer. The agreement component includes GradVac, which aligns gradient directions to mitigate conflicts between source and target domains, and LogitNorm, which regulates logit magnitudes to prevent domination by a single gradient source. The disagreement component consists of a Disagreement Restriction (DiR) and an ensemble strategy, which capture diverse predictive target features and mitigate the loss of critical target information. Extensive experiments demonstrate the effectiveness and superiority of the proposed method in achieving robust and balanced knowledge transfer across heterogeneous HSI scenes.