[1] [1] Jia S, Jiang S, Lin Z, et al. A survey: Deep learning for hyperspectral image classification with few labeled samples[J]. Neurocomputing, 2021, 448: 179-204.

[2] [2] Wang X, Liu J, Chi W, et al. Advances in Hyperspectral Image Classification Methods with Small Samples: A Review[J]. Remote Sensing, 2023, 15(15): 3795.

[3] [3] Li Y, Zhang H, Shen Q. Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network[J]. Remote Sensing, 2017, 9(1): 67.

[4] [4] Zhong Z, Li J, Luo Z, et al. Spectral-spatial residual network for hyperspectral image classification: A 3-D deep learning framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 56(2): 847-858.

[5] [5] Xu Y, Li Z, Li W, et al. Dual-channel residual network for hyperspectral image classification with noisy labels[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-11.

[8] [8] Song Y, Wang T, Cai P, et al. A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities[J]. ACM Computing Surveys, 2023, 55(13s): 1-40.

[9] [9] Parnami A, Lee M. Learning from few examples: A summary of approaches to few-shot learning[J]. arXiv preprint arXiv: 2203.04291, 2022.

[10] [10] Li Z, Liu M, Chen Y, et al. Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-18.

[11] [11] Zhang Y, Li W, Zhang M, et al. Graph information aggregation cross-domain few-shot learning for hyperspectral image classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(2): 1912-1925.

[12] [12] Liu Q, Peng J, Ning Y, et al. Refined Prototypical Contrastive Learning for Few-Shot Hyperspectral Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-14.

[13] [13] Zhang L, Deng Z, Kawaguchi K, et al. How does mixup help with robustness and generalization?[J]. arXiv preprint arXiv: 2010.04819, 2020.

[14] [14] Xu M, Zhang J, Ni B, et al. Adversarial domain adaptation with domain mixup[C]//Proceedings of the AAAI conference on artificial intelligence. 2020, 34(04): 6502-6509.

[15] [15] Chen T, Kornblith S, Norouzi M, et al. A simple framework for contrastive learning of visual representations[C]//International conference on machine learning. PMLR, 2020: 1597-1607.

[16] [16] He K, Fan H, Wu Y, et al. Momentum contrast for unsupervised visual representation learning[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 9729-9738.

[17] [17] Chen X, He K. Exploring simple siamese representation learning[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 15750-15758.

[18] [18] Grill J B, Strub F, Altch F, et al. Bootstrap your own latent-a new approach to self-supervised learning[J]. Advances in neural information processing systems, 2020, 33: 21271-21284.

[19] [19] Melgani F, Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines[J]. IEEE Transactions on geoscience and remote sensing, 2004, 42(8): 1778-1790.