Spectral-spatial multi-view contrastive learning for hyperspectral image classification

The classification of hyperspectral images (HSI) is fundamental to applications such as resource exploration, environmental monitoring, and precision agriculture. However, the scarcity of labeled samples caused by the high cost of manual annotation severely limits the performance of supervised HSI classification models. To address this issue, self-supervised learning (SSL), particularly contrastive learning (CL), has attracted increasing attention due to its ability to leverage large amounts of unlabeled data. Nevertheless, existing CL methods typically rely on data augmentation to construct positive pairs, which is often ineffective or inappropriate for hyperspectral pixel-level samples. To overcome these limitations, we propose a novel Multi-view Contrastive Learning (MVCL) framework for HSI classification, which performs contrastive learning by explicitly exploiting multiple complementary views of the same hyperspectral sample, rather than relying on handcrafted data augmentations. By maximizing mutual information across different views during pre-training, MVCL learns more discriminative and robust spectral–spatial representations. Furthermore, we design a Spatial Aggregation-based Attention (SPAA) module to enhance the transformer backbone within the contrastive learning framework. The SPAA module integrates convolutional spatial aggregation into the self-attention mechanism, enabling more effective local feature modeling while significantly reducing computational complexity. In addition, a supervised classification loss is jointly incorporated to further enforce intra-class compactness and inter-class separability during fine-tuning. Extensive experiments on three widely used hyperspectral benchmark datasets demonstrate that the proposed MVCL framework consistently maintains competitive performance. Specifically, the proposed approach achieves Overall Accuracies of 93.58%, 94.87% and 99.15% compared to the ViT baseline, the improvements are 15.63%, 3.38% and 18.08% respectively, confirming the effectiveness and generalization capability of the proposed multi-view contrastive learning strategy. The source code is publicly available at: https://github.com/yangzhengrui732/MVCL.git .