Uncertainty-Guided Multi-Center Prototype Alignment for Cross-Domain Few-Shot Hyperspectral Image Classification

Abstract

Accurate hyperspectral image analysis plays a critical role in environmental monitoring, precision agriculture, and urban mapping, yet acquiring large-scale annotated datasets for newly emerging scenes and sensors remains challenging. Cross-domain few-shot hyperspectral image classification addresses this bottleneck by transferring knowledge from a labeled source domain to a sparsely annotated target domain. Existing prototype-based approaches commonly use a single-mean prototype per class, which is often inadequate for target classes with spectral–spatial heterogeneity, intra-class dispersion, and unstable episode-level statistics. Consequently, the resulting alignment reference may fail to accurately characterize class structure, thereby exacerbating negative transfer under domain shift. To address this issue, we propose a plug-and-play prototype-stability module that combines Uncertainty-Guided Clustered Alignment (UGCA) with Center Regularization. UGCA identifies hard classes using a class-level dispersion proxy and dynamically constructs multi-center prototypes to better capture intra-class heterogeneity beyond the single-mean assumption. Meanwhile, Center Regularization adds a lightweight compactness constraint on query embeddings to reduce prototype drift under sparse supervision. Experiments on three cross-domain tasks demonstrate the effectiveness of the proposed method. Compared with the reproduced MLPA baseline over 10 independent runs, the proposed method improves the mean OA from 69.21 ± 2.71%, 81.90 ± 3.45%, and 76.92 ± 1.10% to 71.24 ± 3.39%, 83.73 ± 3.89%, and 78.24 ± 1.55% on the Indian Pines (IP), University of Pavia (UP), and Houston (HT) target domains, respectively. Furthermore, cross-framework insertion into Gia-CFSL further verifies that the module is host-agnostic across prototype-driven CD-FSL frameworks and improves prototype-based hyperspectral image analysis without changing the inference pipeline. These results indicate that improving prototype quality is a critical and complementary dimension for robust cross-domain few-shot hyperspectral classification under sparse supervision.