[1] H. S. to Bühne and N. Pettorelli, “Better together: Integrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science,” Methods Ecol. Evol., vol. 9, no. 4, pp. 849–865, 2018.
[2] C.-I. Chang, Hyperspectral imaging: techniques for spectral detection and classification, vol. 1. Springer Science & Business Media, 2003.
[3] G. Pariani et al., “Compressive sampling for multispectral imaging in the vis-NIR-TIR: optical design of space telescopes,” in Space Telescopes and Instrumentation 2018: Optical, Infrared, and Millimeter Wave, 2018, vol. 10698, p. 106985O.
[4] Q. Yuan, L. Zhang, and H. Shen, “Hyperspectral Image Denoising With a Spatial-Spectral View Fusion Strategy.,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 5, pp. 2314–2325, 2014.
[5] L. Sun and J. Luo, “Junk band recovery for hyperspectral image based on curvelet transform,” J. Cent. South Univ. Technol., vol. 18, no. 3, pp. 816–822, 2011.
[6] G. Lantzanakis, Z. Mitraka, and N. Chrysoulakis, “Comparison of physically and image based atmospheric correction methods for Sentinel-2 satellite imagery,” in Perspectives on Atmospheric Sciences, Springer, 2017, pp. 255–261.
[7] A. Zelinski and V. Goyal, “Denoising hyperspectral imagery and recovering junk bands using wavelets and sparse approximation,” in Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on, 2006, pp. 387–390.
[8] E. Martel et al., “Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons,” Remote Sens., vol. 10, no. 6, p. 864, 2018.
[9] P. T. Fazila and D. A. K. Mohideen, “A novel approach for hyperspectral image mixed noise reduction based on improved K-SVD algorithm,” Int. J. Emerg. Technol. Adv. Eng, vol. 4, no. 3, pp. 76–83, 2014.
[10] H. Zhang, W. He, L. Zhang, H. Shen, and Q. Yuan, “Hyperspectral image restoration using low-rank matrix recovery,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 8, pp. 4729–4743, 2014.
[11] W. Wei, L. Zhang, Y. Jiao, C. Tian, C. Wang, and Y. Zhang, “Intracluster Structured Low-Rank Matrix Analysis Method for Hyperspectral Denoising,” IEEE Trans. Geosci. Remote Sens., no. 99, pp. 1–15, 2018.
[12] P. Scheunders and S. De Backer, “Wavelet denoising of multicomponent images using Gaussian scale mixture models and a noise-free image as priors,” IEEE Trans. Image Process., vol. 16, no. 7, pp. 1865–1872, 2007.
[13] B. Rasti, J. R. Sveinsson, M. O. Ulfarsson, and J. A. Benediktsson, “Hyperspectral image denoising using first order spectral roughness penalty in wavelet domain,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, vol. 7, no. 6, pp. 2458–2467, 2014.
[14] H. Yang et al., “Application and evaluation of wavelet-based denoising method in hyperspectral imagery data,” in International Conference on Computer and Computing Technologies in Agriculture, 2011, pp. 461–469.
[15] T. Lin and S. Bourennane, “Survey of hyperspectral image denoising methods based on tensor decompositions,” EURASIP J. Adv. Signal Process., vol. 2013, no. 1, p. 186, Dec. 2013.
[16] L. Xu, F. Li, A. Wong, and D. A. Clausi, “Hyperspectral image denoising using a spatial--spectral monte carlo sampling approach,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 8, no. 6, pp. 3025–3038, 2015.
[17] A. Lam, I. Sato, and Y. Sato, “Denoising hyperspectral images using spectral domain statistics,” in Pattern Recognition (ICPR), 2012 21st International Conference on, 2012, pp. 477–480.
[18] H. Zhang, “Hyperspectral image denoising with cubic total variation model,” ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., vol. 7, pp. 95–98, 2012.
[19] L. Sun, B. Jeon, Z. Wu, and L. Xiao, “Hyperspectral Denoising Via Cross Total Variation-Regularized Unidirectional Nonlocal Low-Rank Tensor Approximation,” in 2018 25th IEEE International Conference on Image Processing (ICIP), 2018, pp. 2900–2904.
[20] https://earthexplorer.usgs.gov/
[22] L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D nonlinear Phenom., vol. 60, no. 1–4, pp. 259–268, 1992
[23] J. J. Benedetto, W. Czaja, M. Ehler, C. Flake, and M. Hirn, “Wavelet packets for multi-and hyper-spectral imagery,” in Wavelet Applications in Industrial Processing VII, 2010, vol. 7535, p. 753508.
[24] P. S. Addison, The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. CRC press, 2017.
[25] N. Verma and A. K. Verma, “Performance analysis of wavelet thresholding methods in denoising of audio signals of some Indian Musical Instruments,” Int. J. Eng. Sci. Technol., vol. 4, no. 5, pp. 2040–2045, 2012.
[26] S. Li, X. Kang, L. Fang, J. Hu, and H. Yin, “Pixel-level image fusion: A survey of the state of the art,” Inf. Fusion, vol. 33, pp. 100–112, 2017.
[27] H. Peng, “Automatic Denoising and Unmixing in Hyperspectral Image Processing,” 2014.