A Multimodal Prototype Correction and Multidimensional Knowledge Distillation Framework for Cross-Domain Few-Shot Hyperspectral Image Classification

Prototype-driven few-shot learning holds promise for cross-domain hyperspectral image classification but encounters two key limitations: (1) over-reliance on visual features overlooks deep semantic information, hindering the differentiation of visually similar categories; and (2) inadequate semantic connections between local and holistic features restrict the utilization of spatial-spectral characteristics. To tackle these issues, we propose a multi-modal prototype correction and multi-dimensional knowledge distillation framework for cross-domain few-shot hyperspectral image classification (MCMD-CFSL). This framework implements a multi-modal prototype correction strategy that leverages pre-trained language models to extract spatial and spectral text features from spatial and spectral text descriptions, thereby aligning image spatial prototypes with spatial text features and spectral prototypes with spectral text features to enhance the recognition of diverse ground objects. Additionally, a multi-dimensional knowledge distillation mechanism is developed to ensure semantic consistency between local and holistic features across spatial-spectral dimensions, bolstering the model’s discriminative power and generalization ability. Experimental results demonstrate that MCMD-CFSL significantly surpasses existing deep learning and few-shot learning techniques on four hyperspectral datasets. The code is available at https://github.com/Li-ZK/MCMD-CFSL-2025.