Few-Shot Learning Based on Multilevel Contrast for Cross-Domain Hyperspectral Image Classification

Recently, domain adaptation techniques have been introduced for cross-domain few-shot hyperspectral image (HSI) classification tasks, but effectively aligning the source and target domains remains a core challenge. Current domain adaptation methods focus mainly on the instance-level, which can easily damage the category separability of the target domain features. To address this, we propose a cross-domain multilevel contrast (CDMLC) method. We implement cross-domain few-shot learning (FSL) at the instance-level, category-level, and category-distribution-level. At the instance-level, meta-learning is employed to capture meta-knowledge. At the category-level, we design an intradomain contrastive loss with domain-mixed dictionaries. The intradomain contrastive loss can ensure feature compactness within each category in both domains and similar feature representations for semantically similar categories across domains. At the category-distribution-level, we perform category distribution fitting by supervised and unsupervised Gaussian mixture models (GMMs) in the source and target domains, respectively. Interdomain contrastive loss is constructed on the basis of the category distribution to promote domain alignment. Extensive experiments are conducted on five publicly available target HSI datasets, and the results demonstrate that our method outperforms the state-of-the-art FSL methods. The code is available at https://github.com/AAAA-CS/CDMLC.