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Canadian Journal of Remote Sensing
Journal canadien de télédétection
Volume 46, 2020 - Issue 1
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Original Articles

Fast Unmixing of Noisy Hyperspectral Images Based on Vertex Component Analysis and Singular Spectrum Analysis Algorithms

Séparation spectrale rapide d’images hyperspectrales bruitées basée sur les algorithmes de l’analyse des composantes vertex et de l’analyse du spectre singulier

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Pages 34-48 | Received 09 Jul 2019, Accepted 14 Jan 2020, Published online: 25 Mar 2020

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.

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