References
- Bioucas-Dias, J.M., Plaza, A., and Dobigeon, N. 2012. “Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 5(No. 2):354–379. doi:10.1109/JSTARS.2012.2194696.
- Chen, Q.L., and Xue, Y.Q. 2000. “Estimation of signal-noise-ratio from data acquired with OMIS.” Journal of Remote Sensing, (No. 04):284–289.
- Das, S., Routray, A., and Deb, A. 2018. “Fast semi-supervised unmixing of hyperspectral image by mutual coherence reduction and recursive PCA.” Remote Sensing, Vol. 10(No. 7):1106. doi:10.3390/rs10071106.
- Donoho, D.L., and Johnstone, J.M. 1994. “Ideal spatial adaptation by wavelet shrinkage.” Biometrika, Vol. 81(No. 3):425–455. doi:10.1093/biomet/81.3.425.
- Downie, T.R., and Silverman, B.W. 1998. “The discrete multiple wavelet transform and thresholding methods.” IEEE Transactions on Signal Processing, Vol. 46(No. 9):2558–2561. doi:10.1109/78.709546.
- Feng, X.-R., Li, H.-C., Li, J., Du, Q., Plaza, A., and Emery, W.J. 2018. “Hyperspectral unmixing using sparsity-constrained deep nonnegative matrix factorization with total variation.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 56(No. 10):6245–6257. doi:10.1109/TGRS.2018.2834567.
- Gao, L.R., Zhang, B., and Zhang, X. 2007. “Study on the method for estimating the noise in remote sensing images based on local standard deviations.” Journal of Remote Sensing, (No. 02):201–208.
- García Plaza, E., and Núñez López, P.J. 2017. “Núñez López. Surface roughness monitoring by singular spectrum analysis of vibration signals.” Mechanical Systems and Signal Processing, Vol. 84: 516–530. doi:10.1016/j.ymssp.2016.06.039.
- Hapke, B. 1981. “Bidirectional reflectance spectroscopy: 1. Theory.” Journal of Geophysical Research: Solid Earth, Vol. 86(No. B4):3039–3054. doi:10.1029/JB086iB04p03039.
- Hapke, B. 1983. Theory of Reflectance and Emittance Spectroscopy. Cambridge, U.K: Cambridge University Press.
- Hassani, H. 2007. “Singular spectrum analysis: Methodology and comparison.” Journal of Data Science, Vol. 5(No. 2):239–257.
- Henry, E.R. 1992. “Singular value decomposition: Application to analysis of experimental data.” Methods in Enzymology, Vol. 210(No. 1):129–192.
- Ichoku, C., and Karnieli, A. 1996. “A review of mixture modeling techniques for sub-pixel land cover estimation.” Remote Sensing Reviews, Vol. 13(No. 3–4):161–186. doi:10.1080/02757259609532303.
- Ji, B., and Chang, C. 2006. “Weighted least squares error approaches to abundance-constrained linear spectral mixture analysis.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 44(No. 2):378–388. doi:10.1117/12.604102.
- Keshava, N., and Mustard, J.F. 2002. “Spectral unmixing.” IEEE Signal Processing Magazine, Vol. 19(No. 1):44–57. doi:10.1109/79.974727.
- Lee, Z., and Carder, K.L. 2012. “Hyperspectral remote sensing.” Current Science, Vol. 94(No. 9):1115–1116.
- Levey, A., and Lindenbaum, M. 2000. “Sequential Karhunen-Loeve basis extraction and its application to images.” IEEE Transactions on Image Processing, Vol. 9(No. 8):1371–1374. doi:10.1109/83.855432.
- Li, J., Agathos, A., and Zaharie, D. 2015. “Minimum volume simplex analysis: A fast algorithm for linear hyperspectral unmixing.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 53(No. 9):1–16. doi:10.1109/TGRS.2015.2417162.
- Li, J., Bioucas-Dias, J.M., Plaza, A., and Liu, L. 2016. “Robust collaborative nonnegative matrix factorization for hyperspectral unmixing.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 54(No. 10):6076–6090. doi:10.1109/TGRS.2016.2580702.
- Lopez, S., Horstrand, P., Callico, G.M., Lopez, J.F., and Sarmiento, R. 2012. “A low-computational-complexity algorithm for hyperspectral endmember extraction: Modified vertex component analysis.” IEEE Geoscience and Remote Sensing Letters, Vol. 9(No. 3):502–506. doi:10.1109/LGRS.2011.2172771.
- Ma, W.-K., Bioucas-Dias, J.M., Chan, T.-H., Gillis, N., Gader, P., Plaza, A.J., Ambikapathi, A., et al. 2014. “A signal processing perspective on hyperspectral unmixing: Insights from remote sensing.” IEEE Signal Processing Magazine, Vol. 31(No. 1):67–81. doi:10.1109/MSP.2013.2279731.
- Myung, N. K. 2009. Singular Spectrum Analysis. Berlin, Germany: Springer; Vol. 1283(No.4):932–942.
- Nascimento, J.M.P., and Dias, J.M.B. 2005. “Vertex component analysis: A fast algorithm to unmix hyperspectral data.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 43(No. 4):898–910. doi:10.1109/TGRS.2005.844293.
- Plaza, A., and Chang, C. 2005. “An improved N-FINDR algorithm in implementation.” Proceedings of SPIE - The International Society for Optical Engineering, Vol. 5806:298–306. doi:10.1117/12.602373.
- Qiao, T., Ren, J., Wang, Z., Zabalza, J., Sun, M., Zhao, H., Li, S., Benediktsson, J.A., Dai, Q., and Marshall, S. 2017. “Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 55(No. 99):119–133. doi:10.1109/TGRS.2016.2598065.
- Ramachandran, P.A. 2002. “Method of fundamental solutions: Singular value decomposition analysis.” Communications in Numerical Methods in Engineering, Vol. 18(No. 11):789–801. doi:10.1002/cnm.537.
- Savas, O., Berk, K., and Bozdagi, A.G. 2018. “EndNet: Sparse autoencoder network for endmember extraction and hyperspectral unmixing.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 57(No. 1):482–496.
- Vautard, R., Yiou, P., and Ghil, M. 1992. “Singular-spectrum analysis: A toolkit for short, noisy chaotic signals.” Physica D: Nonlinear Phenomena, Vol. 158(No. 1–4):95–126. doi:10.1016/0167-2789(92)90103-T.
- Verhoef, W. 1984. “Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model.” Remote Sensing of Environment, Vol. 16(No. 2):125–141. doi:10.1016/0034-4257(84)90057-9.
- Wang, B., Shi, W., and Miao, Z. 2014. “Comparative Analysis for Robust Penalized Spline Smoothing Methods.” Mathematical Problems in Engineering, Vol. 7(No. 16): 339– 353. doi:10.1155/2014/642475.
- Winter, M.E. 2004. “A proof of the N-FINDR algorithm for the automated detection of endmembers in a hyperspectral image.” Proceedings of SPIE - The International Society for Optical Engineering, Vol. 5425:31–41. doi:10.1117/12.542854.
- Zhang, S., Agathos, A., and Li, J. 2017. “Robust minimum volume simplex analysis for hyperspectral unmixing.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 55(No. 11):6431–6439.
- Zhang, B., Sun, X., Gao, L.-R., and Yang, L.-N. 2011. “A method of endmember extraction in hyperspectral remote sensing images based on discrete particle swarm optimization (D-PSO)”. Guang pu ue yu uang pu en xi = Guang Pu, Vol. 31(No. 9):2455–2461.
- Zhang, L., Zhang, L., Du, B., You, J., and Tao, D. 2019. “Hyperspectral image unsupervised classification by robust manifold matrix factorization.” Information Sciences, Vol. 485:154–169. doi:10.1016/j.ins.2019.02.008.
- Zhang, L., Zhang, Q., Du, B., Huang, X., Tang, Y.Y., and Tao, D. 2018. “Simultaneous spectral-spatial feature selection and extraction for hyperspectral images.” IEEE Transactions on Cybernetics, Vol. 48(No. 1):16–28. doi:10.1109/TCYB.2016.2605044.