[1] [1] Chang C, Du Q. Estimation of number of spectrally distinct signal sources in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(3): 608-619.

[2] [2] Xu Ping, Xiao Chong, Zhang Jingcheng, et al. Determination of plant hyperspectral data based on grouped three-dimensional discrete cosine transform dictionary [J]. Acta Optica Sinica, 2017(6): 0630003. (in Chinese)

[3] [3] Xu Dong, Sun Lei, Luo Jianshu. Hetero-spectral hyperspectral remote sensing image denoising combined with NAPCA and complex wavelet transform [J]. Infrared and Laser Engineering, 2015, 44(1): 327-334.

[4] [4] Huang Wei, Gao Taichang, Liu Lei, et al. A method for noise reduction of hyperspectral data based on improved cumulative variance [J]. Spectroscopy and Spectral Analysis, 2016, 36(11): 3625-3629. (in Chinese)

[5] [5] Yu Zhenmiao, Yang Ming. High spectral image denoising algorithm combining spatial similarity and RPCA [J]. Journal of Nanjing University (Natural Science), 2017, 53(3): 518-524. (in Chinese)

[6] [6] Zhang H, He W, Zhang L, et al. Hyperspectral image restoration using low-rank matrix recovery[J]. IEEE Transactions on Geoscience & Remote Sensing, 2014, 52(8): 4729-4743.

[7] [7] Almansa A, Durand S, Rougé B. Measuring and improving image resolution by adaptation of the reciprocal cell[J]. Journal of Mathematical Imaging and Vision, 2004, 21(3):235-279.

[8] [8] Zhang Zhi, Xia Deshen. Image restoration of reciprocal cell combining with complex wavelet[J]. Acta Electronica Sinica, 2008, 36(10): 1979-1985. (in Chinese)

[9] [9] Zhang Yuhui, Tang Yang, Chen Qiang, et al. Improve the effective resolution of oblique-mode remote sensing image [J]. Journal of Computer-Aided Design and Graphics, 2009, 21(2): 243-249. (in Chinese)

[10] [10] Zhang Aiwu, Du Nan, Kang Xiaoyan, et al. Non-linear transformation and information-related hyperspectral adaptive band selection [J]. Infrared and Laser Engineering, 2017, 46(5): 0538001. (in Chinese)

[11] [11] Wu Z, Wang Q, Jin J, et al. Structure tensor total variation-regularized weighted nuclear norm minimization for hyperspectral image mixed denoising[J]. Signal Processing, 2017, 131(1): 202-219.

[12] [12] Yang Xinfeng, Hu Xunuo, Nian Yongjian. High-spectral image compression algorithm based on classification [J]. Infrared and Laser Engineering, 2016, 45(2): 0228003.

[13] [13] Tang Zhongqi, Fu Guangyuan, Chen Jin, et al. Sparse representation and classification of hyperspectral images based on multi-scale segmentation[J]. Optical Precision Engineering, 2015, 23(9): 2708-2714. (in Chinese)

[14] [14] Chen Z, Karim M A, Hayat M M. Elimination of higher order aliasings by multiple interlaced sampling[J]. Optical Engineering, 1999, 38(38): 879.

[15] [15] He Yanfei. Remote sensing image restoration based on sparse representation and adaptive inverse cell [D]. Nanjing: Nanjing University of Information Science and Technology, 2015.

[16] [16] Zhang Zhi, Xia Deshen. Image resolution based on hybrid reciprocal cell-wavelet[J]. Journal of Computer-Aided Design and Computer Graphics, 2008, 20(4): 512-519. (in Chinese)

[17] [17] Damien Muti, Salah Bourennane. Multidimensional filtering based on a tensor approach[J]. Signal Process, 2005, 85(12): 2338-2353.

[18] [18] Damien Muti, Salah Bourennane. Noise removal from hyperspectral images by multidimensional filtering[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 46(7): 2061-2069.

[19] [19] Wang Zhongmei, Yang Xiaomei, Gu Xingfa. High-spectral image denoising algorithm of tensor groups[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(5): 614-622. (in Chinese)