[1] J M BIOUCAS-DIAS et al. Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 354-379(2012).

[3] R Y FENG, L Z WANG, T Y ZENG. Review of hyperspectral remote sensing image subpixel information extraction. Acta Geodaetica et Cartographica Sinica, 52, 1187-1201(2023).

[4] J H LAN, J L ZOU, Y S HAO et al. Research progress on unmixing of hyperspectral remote sensing imagery. Journal of Remote Sensing, 22, 13-27(2018).

[5] B RASTI, A ZOUAOUI, J MAIRAL et al. Image processing and machine learning for hyperspectral unmixing: an overview and the HySUPP python package. IEEE Transactions on Geoscience and Remote Sensing, 62, 5517631(2024).

[6] J CHEN, M ZHAO, X WANG et al. Integration of physics-based and data-driven models for hyperspectral Iiage unmixing: a summary of current methods. IEEE Signal Processing Magazine, 40, 61-74(2023).

[7] Y C SU, R Q XU, L R GAO. Development of deep learning-based hyperspectral remote sensing image unmixing. National Remote Sensing Bulletin, 28, 1-19(2024).

[8] B PALSSON, J R SVEINSSON, M O ULFARSSON. Blind hyperspectral unmixing using autoencoders: a critical comparison. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 1340-1372(2022).

[9] S CHEN, W GUO. Auto-encoders in deep learning—a review with new perspectives. Mathematics, 11, 1777(2023).

[10] K BERAHMAND, F DANESHFAR, E S SALEHI et al. Autoencoders and their applications in machine learning: a survey. Artif Intell Rev, 57, 10662-10668(2024).

[11] GUO R, WANG W, QI H. Hyperspectral image unmixing using autoencoder cade [C]Wkshop on Hyperspectral Image Signal Processing: Evolution in Remote Sensing, 2015: 14.

[12] PALSSON F, SIGURDSSON J, SVEINSSON J R, et al. Neural wk hyperspectral unmixing with spectral infmation divergence objective [C]IEEE International Geoscience Remote Sensing Symposium (IGARSS), 2017: 755758.

[13] S OZKAN, B KAYA, G B AKAR. EndNet: sparse autoencoder network for endmember extraction and hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 57, 482-496(2019).

[14] B PALSSON, J SIGURDSSON, J R SVEINSSON et al. Hyperspectral unmixing using a neural network autoencoder. IEEE Access, 6, 25646-25656(2018).

[15] Y SU, A MARINONI, J LI, J PLAZA et al. Stacked nonnegative sparse autoencoders for robust hyperspectral unmixing. IEEE Geoscience and Remote Sensing Letters, 15, 1427-1431(2018).

[16] Y QU, H QI. UDAS: an untied denoising autoencoder with sparsity for spectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 57, 1698-1712(2019).

[17] B PALSSON, J R SVEINSSON. , ULFARSSON M O. Spectral-spatial hyperspectral unmixing using multitask learning. IEEE, 7, 148861-148872(2019).

[18] Y SU, J LI, A PLAZA et al. DAEN: deep autoencoder networks for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 57, 4309-4321(2019).

[19] Q JIN, Y MA, X MEI et al. TANet: an unsupervised two-stream autoencoder network for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 60, 5506215(2022).

[20] X ZHANG, Y SUN, J ZHANG et al. Hyperspectral unmixing via deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 15, 1755-1759(2018).

[21] B PALSSON, M O ULFARSSON, J R SVEINSSON. Convolutional autoencoder for spectral–spatial hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 59, 535-549(2021).

[22] M ZHAO, S SHI, J CHEN et al. A 3-D-CNN framework for hyperspectral unmixing with spectral variability. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-14(2022).

[23] B RASTI, B KOIRALA. SUnCNN: sparse unmixing using unsupervised convolutional neural network. IEEE Geoscience and Remote Sensing Letters, 19, 5508205(2022).

[24] M ZHAO, L YAN, J CHEN. LSTM-DNN based autoencoder network for nonlinear hyperspectral image unmixing. IEEE Journal of Selected Topics in Signal Processing, 15, 295-309(2021).

[25] LEI M, LI J, QI L, et al. Hyperspectral unmixing via recurrent neural wk with chain classifier [C]2020 IEEE International Geoscience Remote Sensing Symposium, 2020: 21732176.

[26] X E YOU, Y C SU, M Y JIANG. Deep embedded Transformer network with spatial-spectral information for unmixing of hyperspectral remote sensing images. Journal of Image and Graphics, 29, 2220-2235(2024).

[27] P GHOSH, S K ROY, B KOIRALA et al. Hyperspectral unmixing using Transformer network. IEEE Transactions on Geoscience and Remote Sensing, 60, 5535116(2022).

[28] R A BORSOI, T IMBIRIBA, J C M BERMUDEZ. Deep generative endmember modeling: An application to unsupervised spectral unmixing. IEEE Transactions on Computational Imaging, 6, 374-384(2020).

[29] A MIN, Z GUO, H LI et al. JMnet: Joint metric neural network for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 60, 5505412(2022).

[30] VILLANI C. The Wasserstein Distances [M]. Germany: Springer, 2009: 93–111.

[31] ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein generative adversarial wks [C]Proceedings of the 34th International Conference on Machine Learning, 2017, 70: 214–223.

[32] Q JIN, Y MA, F FAN et al. Adversarial autoencoder network for hyperspectral unmixing. IEEE Transactions on Neural Networks and Learning Systems, 34, 4555-4569(2023).

[33] Z HAN, D HONG, L GAO et al. AutoNAS: Automatic neural architecture search for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 60, 3186480(2022).

[34] HAN Z, HONG D, GAO L, et al. EvoNAS: evolvable neural architecture search f hyperspectral unmixing [C]IEEE International Geoscience Remote Sensing Symposium, 2021: 33253328.

[35] M ZHAO, L TANG, J CHEN. Unrolling plug-and-play network for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 63, 5506113(2025).

[36] L ZHU, K QIN, M LI et al. Research on improved stacked sparse autoencoders for mineral hyperspectral endmember extraction. Spectroscopy and Spectral Analysis, 41, 1288-1293(2021).

[37] K QIN, F Y GE, Y J ZHAO et al. Hapke data augmentation for deep learning-based hyperspectral data analysis with limited samples. IEEE Geoscience and Remote Sensing Letters, 18, 886-890(2021).

[38] B RASTI, B KOIRALA, P SCHEUNDERS. HapkeCNN: blind nonlinear unmixing for intimate mixtures using Hapke model and convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 60, 5536315(2022).

[39] M ZHAO, M WANG, J CHEN et al. Hyperspectral unmixing for additive nonlinear models with a 3-D-CNN autoencoder network. IEEE Transactions on Geoscience and Remote Sensing, 60, 5509415(2022).

[40] M WANG, M ZHAO, J CHEN et al. Nonlinear unmixing of hyperspectral data via deep autoencoder networks. IEEE Geoscience and Remote Sensing Letters, 16, 1467-1471(2019).