[1] Goetz A F, Vane G, Solomon J E et al. Imaging spectrometry for Earth remote sensing[J]. Science, 228, 1147-1153(1985).

[2] Ershat A, Baidengsha M, Mamat S et al. Combined estimation of chlorophyll content in cotton canopy based on hyperspectral parameters and back propagation neural network[J]. Acta Optica Sinica, 39, 0930003(2019).

[3] Qian L Y, Wu D C, Zhou X J et al. Radiation calibration and ground object information acquisition based on high spectral imaging lidar system[J]. Acta Optica Sinica, 40, 1128001(2020).

[4] Sun W W, Yang G, Chen C et al. Development status and literature analysis of China's earth observation remote sensing satellites[J]. Journal of Remote Sensing, 24, 479-510(2020).

[5] Yu C Y, Zhao M, Song M P et al. Hyperspectral image classification method based on targets constraint and spectral-spatial iteration[J]. Acta Optica Sinica, 38, 0628003(2018).

[6] Wu C, Wu Y Q. Target detection in hyperspectral image using projection pursuit based on chaotic particle swarm optimization[J]. Acta Optica Sinica, 31, 1211003(2011).

[7] Li W, Dong M L, Lü N G et al. Multispectral face image registration based on T-distribution mixture model[J]. Acta Optica Sinica, 39, 0710001(2019).

[8] Peng J T, Zhou Y C, Sun W W et al. Self-paced nonnegative matrix factorization for hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 1501-1515(2021).

[9] Sun W W, Peng J T, Yang G et al. Fast and latent low-rank subspace clustering for hyperspectral band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 3906-3915(2020).

[10] Sun W W, Yang G, Peng J T et al. Hyperspectral band selection using weighted kernel regularization[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 3665-3676(2019).

[11] Arad B, Ben-Shahar O. Sparse recovery of hyperspectral signal from natural RGB images. [C]∥European Conference on Computer Vision, October 8-16, 2016, Amsterdam, Holland. Switzerland: Springer, 978, 19-34(2016).

[12] Jia Y, Zheng Y Q, Gu L et al. From RGB to spectrum for natural scenes via manifold-based mapping[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 4715-4723(2017).

[13] Geng Y H, Mei S H, Tian J et al. Spatial constrained hyperspectral reconstruction from RGB inputs using dictionary representation[C]∥2019 IEEE International Geoscience and Remote Sensing Symposium, July 28-August 2, 2019, Yokohama, Japan., 3169-3172(2019).

[14] [14] Xiong ZW, ShiZ, Li HQ, et al.HSCNN: CNN-based hyperspectral image recovery from spectrally undersampled projections[C]∥2017 IEEE International Conference on Computer Vision Workshops (ICCVW), October 22-29, 2017, Venice, Italy. New York: IEEE Press, 2017: 518- 525.

[15] Stiebel T, Koppers S, Seltsam P et al. Reconstructing spectral images from RGB-images using a convolutional neural network[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 18-22, 2018, Salt Lake City, UT, USA., 1061-10615(2018).

[16] Wang L Z, Sun C, Fu Y et al. Hyperspectral image reconstruction using a deep spatial-spectral prior[C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 8024-8033(2019).

[17] Fubara B J, Sedky M, Dyke D. RGB to spectral reconstruction via learned basis functions and weights[C]∥2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 14-19, 2020, Seattle, WA, USA., 1984-1993(2020).

[18] Zhao Y Z, Po L M, Yan Q et al. Hierarchical regression network for spectral reconstruction from RGB images[C]∥2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 14-19, 2020, Seattle, WA, USA., 1695-1704(2020).

[19] Zhang Y L, Tian Y P, Kong Y et al. Residual dense network forimage super-resolution[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 2472-2481(2018).

[20] Roy A G, Navab N, Wachinger C. Recalibrating fully convolutional networks with spatial and channel “squeeze and excitation” blocks[J]. IEEE Transactions on Medical Imaging, 38, 540-549(2019).

[21] Xu P. Study on spectral image reconsturction and display based on multispectral imaging technology[D]. Hangzhou: Zhejiang University(2017).

[22] Yasuma F, Mitsunaga T, Iso D et al. Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum[J]. IEEE Transactions on Image Processing, 19, 2241-2253(2010).