An Entropy-Driven Clustering and Semantic Association Framework for Cross-Domain Few-Shot Hyperspectral Image Classification

Recently, few-shot learning (FSL) has shown promising results in hyperspectral image (HSI) classification. However, in practical applications, insufficient labeled training data makes it difficult to capture the intra-class variation of novel classes, making it challenging for the model to learn inaccurate feature distributions, which in turn leads to inaccurate decision boundaries. To solve this problem, we propose an entropy-driven clustering and semantic association framework (ECSA-FSL). We design a deep semantic association feature enhancement module (FEA), which first explores the potential semantic relationship between the source and target domains, and then constructs a cross-domain feature enhancement strategy to generate more discriminative features. In addition, we employ an entropy-driven clustering mechanism (EDC) to optimize the feature space distribution of the target domain. Our approach achieves remarkable classification accuracy with a small number of samples, particularly excelling in scenarios with high intra-class variability and limited training data. Experiments on two publicly available HSI datasets confirm that ECSA-FSL significantly outperforms existing few-shot learning methods under similar conditions. The code is available at https://github.com/Li-ZK/ECSA-FSL-2025.