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A Multilevel Knowledge Distillation Framework for Cross-Domain Few-Shot Hyperspectral Image Classification
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DOI：10.1109/lgrs.2026.3713326.png)
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Abstract
En 中文
In recent years, hyperspectral image (HSI) classification has garnered widespread attention in remote sensing applications. However, the scarcity of labeled training samples makes class prototypes susceptible to noise and data distribution shifts, which degrades the model’s generalization performance. Furthermore, the insufficiency of labeled samples results in blurred decision boundaries between different classes, often causing misclassification. To address these issues, this letter proposes a multilevel knowledge distillation framework for cross-domain few-shot classification (MLKD-CFSC). The framework consists of two core modules: 1) the feature-level distillation (FD) module and 2) the decision-level distillation (DD) module. The feature distillation module optimizes the student network’s prototype features by leveraging those from a teacher network, which is pre-trained on a large-scale hyperspectral dataset. This process enhances the discriminability and robustness of the features extracted by the student network. Meanwhile, the decision distillation module constructs a knowledge distillation method based on the soft labels of the teacher network, guiding the student network to learn more refined classification decision boundaries by uncovering the potential similarities between categories. Experimental results on two public hyperspectral datasets, Houston (HT) and WHU-Hi-HanChuan (HC), demonstrate that the proposed MLKD-CFSC framework significantly outperforms existing cross-domain few-shot methods, improving the overall accuracy (OA) over the best-performing baseline by 4.00% and 4.47%, respectively. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Li-ZK/MLKD-CFSC-2026</uri>
Keywords:
Decision-level distillation (DD)
feature-level distillation (FD)
few-shot learning (FSL)
hyperspectral image (HSI) classification
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