Overcoming Granularity Mismatch in Knowledge Distillation for Few-Shot Hyperspectral Image Classification

Hyperspectral image classification (HSIC) often struggles due to the scarcity of labeled samples. Knowledge distillation (KD), including self-distillation (SD) where a model learns from its own predictions, has emerged as a promising solution. However, existing distillation methods in HSIC face a “granularity mismatch” problem as they rely on coarse, patch-level data for fine-grained, pixel-level classification, which introduces label noise and causes misclassification. To overcome this issue, we propose central spectral self-distillation (CSSD), a framework that isolates pure spectral information at the patch center and leverages it for SD. CSSD consists of three main components. First, the backbone network separates spectral and spatial feature processing to extract pure central spectral features. Second, a spectral refiner module enhances these spectral features before integrating spatial context. Finally, an SD loss aligns the final predictions with the central spectral guidance, ensuring granularity matching at the pixel level. The experimental results on five hyperspectral datasets demonstrate the effectiveness of CSSD under few-shot conditions. The source code will be available online at https://github.com/ZhaohuiXue/CSSD.