Context-Aware and Contrastiveness-Driven Feature Learning for Cross-Domain Few-Shot Hyperspectral Image Classification

Few-shot learning has attracted considerable attention in the field of hyperspectral image (HSI) classification due to its suitability in addressing the challenges encountered in numerous real-world scenarios. However, the scarcity of labeled samples poses a significant challenge in learning informative and discriminative features, limiting the potential for achieving higher accuracy. In this paper, we propose a contextual information aggregation module (CIAM) as part of the feature extraction network for few-shot hyperspectral image classification which can aggregate more spatial-spectral information for each pixel from the neighbored pixels. Meanwhile, supervised contrastive learning is introduced to learn more discriminative representations for addressing specific challenges of high inter-class similarity and large intra-class variance in hyperspectral images. Extensive experiments on two benchmark datasets show that our proposed method achieves the state-of-the-art results.