메뉴 건너뛰기




Volumn 7, Issue 6, 2014, Pages 1994-2003

Spatial and spectral unmixing using the beta compositional model

Author keywords

Beta compositional model (BCM); endmember; hyperspectral; spatial spectral; spectral variability; unmixing

Indexed keywords

QUADRATIC PROGRAMMING; SPECTROSCOPY;

EID: 84905904297     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2014.2330347     Document Type: Article
Times cited : (82)

References (40)
  • 1
    • 14744289023 scopus 로고    scopus 로고
    • Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability?
    • DOI 10.1016/j.rse.2005.01.002, PII S0034425705000271
    • C. Song, "Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability?" Remote Sens. Environ., vol. 95, no. 2, pp. 248-263, Mar. 2005. (Pubitemid 40328616)
    • (2005) Remote Sensing of Environment , vol.95 , Issue.2 , pp. 248-263
    • Song, C.1
  • 2
    • 85032750976 scopus 로고    scopus 로고
    • Endmember variability in hyperspectral analysis: Addressing spectral variability during spectral unmixing
    • Jan.
    • A. Zare and K. Ho, "Endmember variability in hyperspectral analysis: Addressing spectral variability during spectral unmixing," IEEE Signal Process. Mag., vol. 31, no. 1, pp. 95-104, Jan. 2014.
    • (2014) Signal Process. Mag. , vol.31 , Issue.1 , pp. 95-104
    • Zare, A.1    Ho, K.2
  • 4
    • 84873204153 scopus 로고    scopus 로고
    • Spectral unmixing based on improved extended support vector machines
    • X. Li, L. Wang, and X. Jia, "Spectral unmixing based on improved extended support vector machines," in Proc. Int. Geosci. Remote Sens. Symp. (IGARSS), 2012, pp. 4118-4121.
    • (2012) Proc. Int. Geosci. Remote Sens. Symp. (IGARSS) , pp. 4118-4121
    • Li, X.1    Wang, L.2    Jia, X.3
  • 5
    • 0032156917 scopus 로고    scopus 로고
    • Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models
    • DOI 10.1016/S0034-4257(98)00037-6, PII S0034425798000376
    • D. Roberts et al., "Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models," Remote Sens. Environ., vol. 65, no. 3, pp. 267-279, Sep. 1998. (Pubitemid 29360703)
    • (1998) Remote Sensing of Environment , vol.65 , Issue.3 , pp. 267-279
    • Roberts, D.A.1    Gardner, M.2    Church, R.3    Ustin, S.4    Scheer, G.5    Green, R.O.6
  • 6
    • 42749095036 scopus 로고    scopus 로고
    • Analysis of omega/mars express data hyperspectral data using a multiple-endmember linear spectral unmixing model (MELSUM): Methodology and first results
    • J.-P. Combe et al., "Analysis of omega/mars express data hyperspectral data using a multiple-endmember linear spectral unmixing model (MELSUM): Methodology and first results," Planet. Space Sci., vol. 56, pp. 951-975, 2008.
    • (2008) Planet. Space Sci. , vol.56 , pp. 951-975
    • Combe, J.-P.1
  • 7
    • 0442314406 scopus 로고    scopus 로고
    • Scale dependence of biophysical structure in deforested areas bordering the Tapajós National Forest, Central Amazon
    • DOI 10.1016/j.rse.2003.03.001
    • G. Asner, M. Bustamante, and A. Townsend, "Scale dependence of biophysical structure in deforested areas bordering the Tapajos National Forest, Central Amazon," Remote Sens. Environ., vol. 87, no. 4, pp. 507-520, 2003. (Pubitemid 38184707)
    • (2003) Remote Sensing of Environment , vol.87 , Issue.4 , pp. 507-520
    • Asner, G.P.1    Bustamante, M.M.C.2    Townsend, A.R.3
  • 8
    • 0033890553 scopus 로고    scopus 로고
    • Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis
    • DOI 10.1109/36.841987
    • C. Bateson, G. Asner, and C. Wessman, "Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 2, pp. 1083-1094, Mar. 2000. (Pubitemid 30594771)
    • (2000) IEEE Transactions on Geoscience and Remote Sensing , vol.38 , Issue.II2 , pp. 1083-1094
    • Ann Bateson, C.1    Asner, G.P.2    Wessman, C.A.3
  • 9
    • 79954622955 scopus 로고    scopus 로고
    • Endmember variability in spectral mixture analysis:Areview
    • Jul.
    • B. Somers, G. P. Asner, L. Tits, and P. Coppin, "Endmember variability in spectral mixture analysis:Areview," Remote Sens. Environ., vol. 115, no. 7, pp. 1603-1616, Jul. 2011.
    • (2011) Remote Sens. Environ. , vol.115 , Issue.7 , pp. 1603-1616
    • Somers, B.1    Asner, G.P.2    Tits, L.3    Coppin, P.4
  • 10
    • 77958020857 scopus 로고    scopus 로고
    • A novel approach based on fisher discriminant null space for decomposition of mixed pixels in hyperspectral imagery
    • Oct.
    • J. Jin, B. Wang, and L. Zhang, "A novel approach based on fisher discriminant null space for decomposition of mixed pixels in hyperspectral imagery," IEEE Geosci. Remote Sens. Lett., vol. 7, no. 4, pp. 699-703, Oct. 2010.
    • (2010) IEEE Geosci. Remote Sens. Lett. , vol.7 , Issue.4 , pp. 699-703
    • Jin, J.1    Wang, B.2    Zhang, L.3
  • 11
    • 2942522160 scopus 로고    scopus 로고
    • Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE
    • DOI 10.1016/S0034-4257(03)00135-4
    • P. Dennison and D. Roberts, "Multiple endmember spectral mixture analysis using endmember average RSME," Remote Sens. Environ., vol. 87, no. 2-3, pp. 123-135, Oct. 2003. (Pubitemid 39661164)
    • (2003) Remote Sensing of Environment , vol.87 , Issue.2-3 , pp. 123-135
    • Dennison, P.E.1    Roberts, D.A.2
  • 12
    • 84945308844 scopus 로고    scopus 로고
    • Application of the normal compositional model to the analysis of hyperspectral imagery
    • D. Stein, "Application of the normal compositional model to the analysis of hyperspectral imagery," in Proc. Workshop Adv. Tech. Anal. Remotely Sens. Data, 2003, pp. 44-51.
    • (2003) Proc. Workshop Adv. Tech. Anal. Remotely Sens. Data , pp. 44-51
    • Stein, D.1
  • 14
    • 77952616442 scopus 로고    scopus 로고
    • Bayesian estimation of linear mixtures using the normal compositional model: Application to hyperspectral imagery
    • Jun.
    • O. Eches, N. Dobigeon, C. Mailhes, and J.-Y. Tourneret, "Bayesian estimation of linear mixtures using the normal compositional model: Application to hyperspectral imagery," IEEE Trans. Image Process., vol. 19, no. 6, pp. 1403-1413, Jun. 2010.
    • (2010) IEEE Trans. Image Process. , vol.19 , Issue.6 , pp. 1403-1413
    • Eches, O.1    Dobigeon, N.2    Mailhes, C.3    Tourneret, J.-Y.4
  • 15
    • 84874661274 scopus 로고    scopus 로고
    • Sampling piecewise convex unmixing and endmember extraction
    • Mar.
    • A. Zare, P. Gader, and G. Casella, "Sampling piecewise convex unmixing and endmember extraction," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 3, pp. 1655-1665, Mar. 2013.
    • (2013) IEEE Trans. Geosci. Remote Sens. , vol.51 , Issue.3 , pp. 1655-1665
    • Zare, A.1    Gader, P.2    Casella, G.3
  • 17
    • 0014254846 scopus 로고
    • Probability plotting methods for the analysis of data
    • M. B. Wilk and R. Gnanadesikan, "Probability plotting methods for the analysis of data," Biometrika, vol. 55, no. 1, pp. 1-17, 1968.
    • (1968) Biometrika , vol.55 , Issue.1 , pp. 1-17
    • Wilk, M.B.1    Gnanadesikan, R.2
  • 19
    • 77956867857 scopus 로고    scopus 로고
    • Natick, MA, USA: The Mathworks Inc
    • Matlab Statistics Toolbox 2013a. Natick, MA, USA: The Mathworks Inc.
    • (2013) Matlab Statistics Toolbox
  • 20
    • 33846643684 scopus 로고    scopus 로고
    • Hysens-DAIS 7915/ROSIS imaging spectrometers at DLR
    • May
    • S. Holzwarth et al., "Hysens-DAIS 7915/ROSIS imaging spectrometers at DLR," in Proc. 3rd EARSel. Workshop Imag. Spectrosc., May 2003, pp. 3-14.
    • (2003) Proc. 3rd EARSel. Workshop Imag. Spectrosc. , pp. 3-14
    • Holzwarth, S.1
  • 21
    • 0001927585 scopus 로고
    • On Information and sufficiency
    • S. Kullback and R. A. Leibler, "On Information and sufficiency," Ann. Math. Stat., vol. 22, no. 1, pp. 79-86, 1951.
    • (1951) Ann. Math. Stat. , vol.22 , Issue.1 , pp. 79-86
    • Kullback, S.1    Leibler, R.A.2
  • 22
    • 85032751930 scopus 로고    scopus 로고
    • Spectral unmixing
    • DOI 10.1109/79.974727
    • N. Keshava and J. Mustard, "Spectral unmixing," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 44-57, Jan. 2002. (Pubitemid 34237207)
    • (2002) IEEE Signal Processing Magazine , vol.19 , Issue.1 , pp. 44-57
    • Keshava, N.1    Mustard, J.F.2
  • 23
    • 84861772901 scopus 로고    scopus 로고
    • Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches
    • Apr.
    • J. Bioucas-Dias et al., "Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches," IEEE J. Sel. Topics Appl. Earth Observ., vol. 5, no. 2, pp. 354-379, Apr. 2012.
    • (2012) IEEE J. Sel. Topics Appl. Earth Observ. , vol.5 , Issue.2 , pp. 354-379
    • Bioucas-Dias, J.1
  • 25
    • 0041193422 scopus 로고
    • On approximations involving the beta distribution
    • B. Jhannessona and N. Giria, "On approximations involving the beta distribution," Commun. Stat. Simul. Comput., vol. 24, pp. 489-503, 1995.
    • (1995) Commun. Stat. Simul. Comput. , vol.24 , pp. 489-503
    • Jhannessona, B.1    Giria, N.2
  • 26
    • 3843151477 scopus 로고    scopus 로고
    • Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries
    • Jul.
    • N. Keshava, "Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 7, pp. 1552-1565, Jul. 2004.
    • (2004) IEEE Trans. Geosci. Remote Sens. , vol.42 , Issue.7 , pp. 1552-1565
    • Keshava, N.1
  • 27
    • 0033309449 scopus 로고    scopus 로고
    • Spectral information divergence for hyperspectral image analysis
    • C.-I. Chang, "Spectral information divergence for hyperspectral image analysis," in Proc. IEEE Geosci. Remote Sens. Symp. (IGARSS), 1999, vol. 1, pp. 509-511.
    • (1999) Proc. IEEE Geosci. Remote Sens. Symp. (IGARSS) , vol.1 , pp. 509-511
    • Chang, C.-I.1
  • 28
    • 77951276538 scopus 로고    scopus 로고
    • A robust fuzzy local information c-means clustering algorithm
    • May
    • S. Krinidis and V. Chatzis, "A robust fuzzy local information c-means clustering algorithm," IEEE Trans. Image Process., vol. 19, no. 5, pp. 1328-1337, May 2010.
    • (2010) IEEE Trans. Image Process. , vol.19 , Issue.5 , pp. 1328-1337
    • Krinidis, S.1    Chatzis, V.2
  • 30
    • 0036489378 scopus 로고    scopus 로고
    • A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data
    • DOI 10.1109/42.996338, PII S0278006202040880
    • M. Ahmed, S. Yamany, N. Mohamed, A. Farag, and T. Moriarty, "A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data," IEEE Trans. Med. Imag., vol. 21, no. 3, pp. 193-199, Mar. 2002. (Pubitemid 34630837)
    • (2002) IEEE Transactions on Medical Imaging , vol.21 , Issue.3 , pp. 193-199
    • Ahmed, M.N.1    Yamany, S.M.2    Mohamed, N.3    Farag, A.A.4    Moriarty, T.5
  • 33
    • 84905924436 scopus 로고    scopus 로고
    • Robust fuzzy c-means with spatial information for segmentation of brain magnetic resonance images
    • Jan.
    • S. F. G. Saeed Fazli, "Robust fuzzy c-means with spatial information for segmentation of brain magnetic resonance images," Int. J. Sci. Eng. Invest., vol. 2, no. 12, pp. 100-105, Jan. 2013.
    • (2013) Int. J. Sci. Eng. Invest. , vol.2 , Issue.12 , pp. 100-105
    • Saeed Fazli, S.F.G.1
  • 34
    • 17044378544 scopus 로고    scopus 로고
    • Global linear convergence of an augmented Lagrangian algorithm to solve convex quadratic optimization problems
    • F. Delbos and J. C. Gilbert, "Global linear convergence of an augmented Lagrangian algorithm to solve convex quadratic optimization problems," J. Convex Anal., vol. 12, no. 1, pp. 45-69, 2005.
    • (2005) J. Convex Anal. , vol.12 , Issue.1 , pp. 45-69
    • Delbos, F.1    Gilbert, J.C.2
  • 37
    • 0035273728 scopus 로고    scopus 로고
    • Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery
    • DOI 10.1109/36.911111, PII S0196289201020861
    • D. Heinz and C.-I. Chang, "Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 3, pp. 529-545, Mar. 2001. (Pubitemid 32400422)
    • (2001) IEEE Transactions on Geoscience and Remote Sensing , vol.39 , Issue.3 , pp. 529-545
    • Heinz, D.C.1    Chang, C.-I.2
  • 38
    • 84905909052 scopus 로고    scopus 로고
    • [Online]. Available
    • I. Gerg. (2012). Matlab Hyperspectral Toolbox [Online]. Available: http://sourceforge.net/projects/matlabhyperspec/
    • (2012) Matlab Hyperspectral Toolbox
    • Gerg, I.1
  • 40
    • 84861741372 scopus 로고    scopus 로고
    • Automated extraction of image-based endmember bundles for improved spectral unmixing
    • Apr.
    • B. Somers, M. Zortea, A. Plaza, and G. Asner, "Automated extraction of image-based endmember bundles for improved spectral unmixing," IEEE J. Sel. Topics Appl. Earth Observ., vol. 5, no. 2, pp. 396-408, Apr. 2012.
    • (2012) IEEE J. Sel. Topics Appl. Earth Observ. , vol.5 , Issue.2 , pp. 396-408
    • Somers, B.1    Zortea, M.2    Plaza, A.3    Asner, G.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.