Cross-domain few-shot hyperspectral image classification based on circle loss and dense graph convolution

Deep learning methods have demonstrated exceptional performance in hyperspectral image classification in recent years. Manual annotation is costly in practical applications of hyperspectral images, leading to a scarcity of labelled samples. At the same time, hyperspectral image classification faces challenges such as spectral variations within the object and identical spectra for different objects. However, current hyperspectral image classification methods are unable to extract spectral and spatial contextual features. Under few-shot conditions, the extracted features do not provide adequate discriminative power for both intra-class and inter-class samples. Moreover, traditional few-shot classification methods require the source and target domain data to have similar distributions in the feature space; otherwise, knowledge learned from the source domain cannot effectively transfer to the target domain. To address these shortcomings, we propose enhancing the feature extraction network’s capability to capture contextual features better, improving the discrimination between intra-class and inter-class samples through contrastive learning, and utilizing graph-structured information to alleviate domain shift issues in this paper. We propose a novel few-shot hyperspectral image classification framework (CDFSL-DGCCL) in which a circle contrastive loss function is introduced to reduce the similarity between features of different classes in the target domain and increase the similarity within the same class, thereby optimizing the feature extraction network. We propose a learning strategy combining dense graph convolutional networks and the circle contrastive loss function. Two densely connected graph convolutional networks are used to extract graph-structured information from the source and target domains, and the circle contrastive loss function is employed to optimize the feature similarity with graph-structured information. A new Global Context Module (GC-RCM) is embedded into the feature extraction network (SSCRNet) to capture global spectral and spatial contextual information. Experiments on four publicly available hyperspectral image datasets demonstrate that our method outperforms existing approaches. The code is available at https://github.com/GJINGKANG/CDFSL-DGCCL.