[1] Tong Q X, Zhang B, Zheng L F[M]. Hyperspectral remote sensing: theory, technology and application(2006).

[2] Gong W, Shi S, Chen B W et al. Development and application of airborne hyperspectral LiDAR imaging technology[J]. Acta Optica Sinica, 42, 1200002(2022).

[3] Chen S X, He Y F. Weighted joint sparse representation hyperspectral image classification based on spatial spectrum dictionary[J]. Acta Optica Sinica, 43, 0110002(2023).

[4] Wang L, Cui X Y, Suo J P et al. Calibration technology of lidar system for absolute detection of air temperature[J]. Acta Optica Sinica, 42, 1828001(2022).

[5] Shaw G A, Burke H K. Spectral imaging for remote sensing[J]. Lincoln Laboratory Journal, 14, 3-28(2003).

[6] Jia X X, Guo B F, Ding F C et al. Hyperspectral unmixing based on constrained nonnegative matrix factorization[J]. Acta Photonica Sinica, 50, 0710005(2021).

[7] Wu X M. Research on unmixing technology of hyperspectral remote sensing images based on kernel method[D](2021).

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

[9] Guo R, Wang W, Qi H R. Hyperspectral image unmixing using autoencoder cascade[C](2017).

[10] Su Y C, Li J, Plaza A et al. DAEN: deep autoencoder networks for hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 4309-4321(2019).

[11] Qu Y, Qi H R. uDAS: an untied denoising autoencoder with sparsity for spectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 1698-1712(2019).

[12] Chen M, Weinberger K, Sha F et al. Marginalized denoising auto-encoders for nonlinear representations[C], 1476-1484(2014).

[13] Hua Z Q, Li X R, Qiu Q H et al. Autoencoder network for hyperspectral unmixing with adaptive abundance smoothing[J]. IEEE Geoscience and Remote Sensing Letters, 18, 1640-1644(2021).

[14] Yuan B. NMF hyperspectral unmixing algorithm combined with spatial and spectral correlation analysis[J]. Journal of Remote Sensing, 22, 265-276(2018).

[15] Palsson B, Ulfarsson M O, Sveinsson J R. Convolutional autoencoder for spectral-spatial hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 535-549(2021).

[16] Ranasinghe Y, Herath S, Weerasooriya K et al. Convolutional autoencoder for blind hyperspectral image unmixing[C], 174-179(2021).

[17] Tulczyjew L, Kawulok M, Longépé N et al. A multibranch convolutional neural network for hyperspectral unmixing[J]. IEEE Geoscience and Remote Sensing Letters, 19, 6011105(2022).

[18] Gao L R, Han Z, Hong D F et al. CyCU-net: cycle-consistency unmixing network by learning cascaded autoencoders[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5503914(2022).

[19] Hua Z Q, Li X R, Jiang J F et al. Gated autoencoder network for spectral–spatial hyperspectral unmixing[J]. Remote Sensing, 13, 3147(2021).

[20] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[21] Ozkan S, Kaya B, Akar G B. EndNet: sparse AutoEncoder network for endmember extraction and hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 482-496(2019).

[22] Zheng K, Gao L R, Liao W Z et al. Coupled convolutional neural network with adaptive response function learning for unsupervised hyperspectral super resolution[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 2487-2502(2021).

[23] Yao J, Hong D, Chanussot J et al. Cross-attention in coupled unmixing nets for unsupervised hyperspectral super-resolution[M]. Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020. Lecture notes in computer science, 12374, 208-224(2020).

[24] Qian Y T, Jia S, Zhou J et al. Hyperspectral unmixing via L1/2 sparsity-constrained nonnegative matrix factorization[J]. IEEE Transactions on Geoscience and Remote Sensing, 49, 4282-4297(2011).

[25] He W, Zhang H Y, Zhang L P. Total variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 3909-3921(2017).

[26] Li H Q, Borsoi R A, Imbiriba T et al. Model-based deep autoencoder networks for nonlinear hyperspectral unmixing[J]. IEEE Geoscience and Remote Sensing Letters, 19, 5506105(2022).