Scale-adaptive Knowledge Distillation with Superpixel for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is a critical area in remote sensing with broad applications in geoscience. While deep learning methods have gained popularity for HSI classification, their potential remains underexplored due to limited labeled data. To address this, we propose a scale-adaptive knowledge distillation with superpixel framework that trains deep neural networks using unlabeled samples. The proposed framework incorporates three core components: (1) scale-adaptive superpixel knowledge distillation, (2) bilateral spatial–spectral attention mechanisms, and (3) three-dimensional (3D) hyperspectral data transformation. The distillation module implements self-supervised learning through dynamically generated soft labels based on cross-dimensional similarity metrics. The workflow proceeds through three stages: Initially, spatial–spectral joint distance metrics evaluate the affinity between unlabeled superpixels and target classes. Subsequently, these measurements inform probabilistic soft label assignments for each superpixel cluster. Finally, an end-to-end trainable dense convolutional network with dual attention pathways is refined by optimizing the divergence between the adaptive label distributions and network predictions. Additionally, 3D transformations, including spectral and spatial rotations of the HSI cube, are applied to maximize the utility of labeled data. Experiments on three public HSI data sets demonstrate that the proposed method achieves competitive accuracy and efficiency compared to existing approaches. The implementation code is available at https://github.com/San-dow/Awnsome-SAKDS_HSI.