Dual-View Spatial Consistency Regularization for Few-Shot Hyperspectral Image Classification

Abstract

Hyperspectral image classification (HSIC) remains challenging when only a few labeled pixels are available for each class. Under such label-scarce conditions, deep models easily overfit the limited supervision and often fail to learn perturbation-invariant spatial representations from local patches. To address this issue, this paper proposes a Spatial Consistency Regularization Model (SCRM) for few-shot HSIC. SCRM is built upon a lightweight PCA-based 2D-CNN backbone and introduces a dual-view spatial perturbation mechanism to explicitly regularize prediction and feature stability. For each labeled patch, weakly and strongly perturbed views are generated and jointly optimized. Prediction-level consistency is imposed through a symmetric Kullback-Leibler divergence loss, while feature-level consistency is enforced by a normalized mean squared error constraint. Under 5-shot evaluation, the best SCRM variants improve OA over the strongest baseline by 2.39, 0.75, 1.61, and 2.21 percentage points on Indian Pines, Pavia University, Salinas, and Kennedy Space Center, respectively, while remaining competitive in most 10-shot settings. These results support SCRM as a lightweight supervised strategy for label-scarce hyperspectral image classification.