Cross-Domain Few-Shot Learning Based on Decoupled Knowledge Distillation for Hyperspectral Image Classification

Existing cross-domain few-shot learning (FSL) methods for hyperspectral image (HSI) classification have garnered widespread attention due to their excellent performance in recognizing novel classes. To mitigate domain shift, researchers focus on designing sophisticated domain adaptation (DA) modules to directly apply biased metaknowledge in the target domain (TD). However, this paradigm proves somewhat inadequate in the face of significant differences in distribution. To cope with this dilemma, we adopted a new mindset of treating metaknowledge extraction and debiasing from the source domain (SD) as a synergistic process and proposed a cross-domain FSL framework based on decoupled knowledge distillation for HSI classification (HSIC). In general, to efficiently acquire and utilize unbiased metaknowledge, this framework centralizes on a knowledge distillation (KD) strategy. Through the effective information transfer process, the extraction and debiasing of metaknowledge were integrated into a comprehensive and productive process. Simultaneously, to release the constraints imposed by the coupled logits in the KD process on the knowledge interaction, the decoupled logit interaction (DLI) module is employed in the framework. This module decouples the traditional KD into two controllable components, making a more balanced and comprehensive interaction of task-related knowledge and data-intrinsic knowledge between models. Moreover, to facilitate the extraction of critical discriminative metaknowledge from the abundant redundant information in HSI, the discriminative information refinement (DIR) module is designed to develop distinctive features for similar bands. Extensive experiments on three public HSI datasets exhibited the superior performance of the proposed cross-domain few-shot learning method based on decoupled knowledge distillation for HSIC (DKD-FSL) method in comparison with seven state-of-the-art approaches.