메뉴 건너뛰기




Volumn 49, Issue 11 PART 1, 2011, Pages 4153-4162

Nonlinear unmixing of hyperspectral images using a generalized bilinear model

Author keywords

Bayesian algorithm; bilinear model; Gibbs sampler; hyperspectral imagery; Markov chain Monte Carlo (MCMC) methods; spectral unmixing

Indexed keywords

BAYESIAN ALGORITHMS; BILINEAR MODELS; GIBBS SAMPLER; HYPERSPECTRAL IMAGERY; MARKOV CHAIN MONTE CARLO METHOD; SPECTRAL UNMIXING;

EID: 80455158223     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2010.2098414     Document Type: Article
Times cited : (382)

References (32)
  • 1
    • 85032751930 scopus 로고    scopus 로고
    • Spectral unmixing
    • DOI 10.1109/79.974727
    • N. Keshava and J. F. 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
  • 2
    • 0028427066 scopus 로고
    • Minimum volume transforms for remotely sensed data
    • May
    • M. Craig, Minimum volume transforms for remotely sensed data, IEEE Trans. Geosci. Remote Sens., vol. 32, no. 3, pp. 542-552, May 1994
    • (1994) IEEE Trans. Geosci. Remote Sens , vol.32 , Issue.3 , pp. 542-552
    • Craig, M.1
  • 3
    • 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. C. 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
  • 4
    • 0035392132 scopus 로고    scopus 로고
    • Hyperspectral subpixel target detection using the linear mixing model
    • DOI 10.1109/36.934072, PII S0196289201054961
    • D. Manolakis, C. Siracusa, and G. Shaw, Hyperspectral subpixel target detection using the linear mixing model, IEEE Trans. Geosci. Remote Sens., vol. 39, no. 7, pp. 1392-1409, Jul. 2001 (Pubitemid 32732655)
    • (2001) IEEE Transactions on Geoscience and Remote Sensing , vol.39 , Issue.7 , pp. 1392-1409
    • Manolakis, D.1    Siracusa, C.2    Shaw, G.3
  • 5
    • 12844266861 scopus 로고    scopus 로고
    • Does independent component analysis play a role in unmixing hyperspectral data?
    • DOI 10.1109/TGRS.2004.839806
    • J. M. P. Nascimento and J. M. Bioucas-Dias, Does independent component analysis play a role in unmixing hyperspectral data? IEEE Trans. Geosci. Remote Sens., vol. 43, no. 1, pp. 175-187, Jan. 2005 (Pubitemid 40162736)
    • (2005) IEEE Transactions on Geoscience and Remote Sensing , vol.43 , Issue.1 , pp. 175-187
    • Nascimento, J.M.P.1    Dias, J.M.B.2
  • 6
    • 77952555368 scopus 로고    scopus 로고
    • PCE: Piece-wise convex endmember detection
    • Jun. 2010
    • A. Zare and P. Gader, PCE: Piece-wise convex endmember detection, IEEE Trans. Geosci. Remote Sens., vol. 48, no. 6, pp. 2620-2632, Jun. 2010
    • IEEE Trans. Geosci. Remote Sens , vol.48 , Issue.6 , pp. 2620-2632
    • Zare, A.1    Gader, P.2
  • 7
    • 0001473286 scopus 로고
    • Bidirectional reflectance spectroscopy. I. Theory
    • B. W. Hapke, Bidirectional reflectance spectroscopy. I. Theory, J. Geophys. Res., vol. 86, no. B4, pp. 3039-3054, 1981
    • (1981) J. Geophys. Res , vol.86 , Issue.B4 , pp. 3039-3054
    • Hapke, B.W.1
  • 8
    • 26944497582 scopus 로고    scopus 로고
    • Mapping microphytobenthos biomass by non-linear inversion of visible-infrared hyperspectral images
    • DOI 10.1016/j.rse.2005.07.010, PII S0034425705002361
    • J.-P. Combe, P. Launeau, V. Carrère, D. Despan, V. Méléder, L. Barillé, and C. Sotin, Mapping microphytobenthos biomass by non-linear inversion of visible-infrared hyperspectral images, Remote Sens. Environ., vol. 98, no. 4, pp. 371-387, Oct. 2005 (Pubitemid 41484097)
    • (2005) Remote Sensing of Environment , vol.98 , Issue.4 , pp. 371-387
    • Combe, J.-P.1    Launeau, P.2    Carrere, V.3    Despan, D.4    Meleder, V.5    Barille, L.6    Sotin, C.7
  • 9
    • 70449447668 scopus 로고    scopus 로고
    • Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated-forest hyperspectral data
    • Jun
    • W. Fan, B. Hu, J. Miller, and M. Li, Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated-forest hyperspectral data, Int. J. Remote Sens., vol. 30, no. 11, pp. 2951-2962, Jun. 2009
    • (2009) Int. J. Remote Sens , vol.30 , Issue.11 , pp. 2951-2962
    • Fan, W.1    Hu, B.2    Miller, J.3    Li, M.4
  • 11
    • 0029667294 scopus 로고    scopus 로고
    • Nonlinear spectral mixing in desert vegetation
    • Jan
    • T. Ray and B. Murray, Nonlinear spectral mixing in desert vegetation, Remote Sens. Environ., vol. 55, no. 1, pp. 59-64, Jan. 1996
    • (1996) Remote Sens. Environ , vol.55 , Issue.1 , pp. 59-64
    • Ray, T.1    Murray, B.2
  • 12
    • 0032551509 scopus 로고    scopus 로고
    • Study of the spectral mixture model of soil and vegetation in Po Yang Lake area, China
    • L. Zhang, D. Li, Q. Tong, and L. Zheng, Study of the spectral mixture model of soil and vegetation in PoYang Lake area, China, Int. J. Remote Sens., vol. 19, no. 11, pp. 2077-2084, 1998 (Pubitemid 28534162)
    • (1998) International Journal of Remote Sensing , vol.19 , Issue.11 , pp. 2077-2084
    • Zhang, L.1    Li, D.2    Tong, Q.3    Zheng, L.4
  • 13
    • 70350450305 scopus 로고    scopus 로고
    • Nonlinear mixture model for hyperspectral unmixing
    • L. Bruzzone, C. Notarnicola, and F. Posa, Eds
    • J. M. Bioucas-Dias and J. M. P. Nascimento, Nonlinear mixture model for hyperspectral unmixing, in Proc. SPIE Image Signal Process. Remote Sens. XV, L. Bruzzone, C. Notarnicola, and F. Posa, Eds., 2009, vol. 7477, no. 1, p. 747 70I
    • (2009) Proc. SPIE Image Signal Process. Remote Sens. XV , vol.7477 , Issue.1
    • Bioucas-Dias, J.M.1    Nascimento, J.M.P.2
  • 14
    • 0002081183 scopus 로고
    • Automating spectral unmixing of AVIRIS data using convex geometry concepts
    • J. Boardman, Automating spectral unmixing of AVIRIS data using convex geometry concepts, in Proc. Summ. 4th Annu. JPL Airborne Geosci. Workshop, 1993, vol. 1, pp. 11-14
    • (1993) Proc. Summ. 4th Annu. JPL Airborne Geosci. Workshop , vol.1 , pp. 11-14
    • Boardman, J.1
  • 15
    • 0000385570 scopus 로고    scopus 로고
    • Fast autonomous spectral end-member determination in hyperspectral data
    • Vancouver, BC, Canada, Apr
    • M. Winter, Fast autonomous spectral end-member determination in hyperspectral data, in Proc. 13th Int. Conf. Appl. Geologic Remote Sens., Vancouver, BC, Canada, Apr. 1999, vol. 2, pp. 337-344
    • (1999) Proc. 13th Int. Conf. Appl. Geologic Remote Sens. , vol.2 , pp. 337-344
    • Winter, M.1
  • 16
    • 16444373735 scopus 로고    scopus 로고
    • Vertex component analysis: A fast algorithm to unmix hyperspectral data
    • DOI 10.1109/TGRS.2005.844293
    • J. M. Nascimento and J.M. Bioucas-Dias, Vertex component analysis: A fast algorithm to unmix hyperspectral data, IEEE Trans. Geosci. Remote Sens., vol. 43, no. 4, pp. 898-910, Apr. 2005 (Pubitemid 40476033)
    • (2005) IEEE Transactions on Geoscience and Remote Sensing , vol.43 , Issue.4 , pp. 898-910
    • Nascimento, J.M.P.1    Dias, J.M.B.2
  • 17
    • 0030189048 scopus 로고    scopus 로고
    • On the relationship between spectral unmixing and subspace projection
    • Jul
    • J. Settle, On the relationship between spectral unmixing and subspace projection, IEEE Trans. Geosci. Remote Sens., vol. 34, no. 4, pp. 1045- 1046, Jul. 1996
    • (1996) IEEE Trans. Geosci. Remote Sens , vol.34 , Issue.4 , pp. 1045-1046
    • Settle, J.1
  • 18
    • 46749145829 scopus 로고    scopus 로고
    • Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery
    • Jul
    • N. Dobigeon, J.-Y. Tourneret, and C.-I Chang, Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery, IEEE Trans. Signal Process., vol. 56, no. 7, pp. 2684-2695, Jul. 2008
    • (2008) IEEE Trans. Signal Process , vol.56 , Issue.7 , pp. 2684-2695
    • Dobigeon, N.1    Tourneret, J.-Y.2    Chang, C.-I.3
  • 19
    • 77952616442 scopus 로고    scopus 로고
    • Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery
    • Jun. 2010
    • 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
    • IEEE Trans. Image Process , vol.19 , Issue.6 , pp. 1403-1413
    • Eches, O.1    Dobigeon, N.2    Mailhes, C.3    Tourneret, J.-Y.4
  • 22
    • 33750402597 scopus 로고    scopus 로고
    • Separation of non-negative mixture of non-negative sources using a Bayesian approach and MCMC sampling
    • DOI 10.1109/TSP.2006.880310
    • S. Moussaoui, D. Brie, A. Mohammad-Djafari, and C. Carteret, Separation of non-negative mixture of non-negative sources using a Bayesian approach and MCMC sampling, IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4133-4145, Nov. 2006 (Pubitemid 44637747)
    • (2006) IEEE Transactions on Signal Processing , vol.54 , Issue.11 , pp. 4133-4145
    • Moussaoui, S.1    Brie, D.2    Mohammad-Djafari, A.3    Carteret, C.4
  • 24
    • 77952595305 scopus 로고    scopus 로고
    • Estimating the number of endmembers in hyperspectral images using the normal compositional model and a hierarchical Bayesian algorithm
    • Jun. 2010
    • O. Eches, N. Dobigeon, and J.-Y. Tourneret, Estimating the number of endmembers in hyperspectral images using the normal compositional model and a hierarchical Bayesian algorithm, IEEE J. Sel. Topics Signal Process., vol. 4, no. 3, pp. 582-591, Jun. 2010
    • IEEE J. Sel. Topics Signal Process , vol.4 , Issue.3 , pp. 582-591
    • Eches, O.1    Dobigeon, N.2    Tourneret, J.-Y.3
  • 25
    • 70350493345 scopus 로고    scopus 로고
    • Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery
    • Nov
    • N. Dobigeon, S. Moussaoui, M. Coulon, J.-Y. Tourneret, and A. O. Hero, Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery, IEEE Trans. Signal Process., vol. 57, no. 11, pp. 4355- 4368, Nov. 2009
    • (2009) IEEE Trans. Signal Process , vol.57 , Issue.11 , pp. 4355-4368
    • Dobigeon, N.1    Moussaoui, S.2    Coulon, M.3    Tourneret, J.-Y.4    Hero, A.O.5
  • 26
    • 84873751778 scopus 로고
    • An invariant form for the prior probability in estimation problems
    • Sep
    • H. Jeffreys, An invariant form for the prior probability in estimation problems, Proc. R. Soc. Lond. A, Math. Phys. Sci., vol. 186, no. 1007, pp. 453-461, Sep. 1946
    • (1946) Proc. R. Soc. Lond. A, Math. Phys. Sci , vol.186 , Issue.1007 , pp. 453-461
    • Jeffreys, H.1
  • 28
    • 67649413745 scopus 로고    scopus 로고
    • Joint linear/nonlinear spectral unmixing of hyperspectral image data
    • J. Plaza, A. Plaza, R. Pérez, and P. Martinez, Joint linear/nonlinear spectral unmixing of hyperspectral image data, in Proc. IEEE IGARSS, 2007, pp. 4037-4040
    • (2007) Proc IEEE IGARSS , pp. 4037-4040
    • Plaza, J.1    Plaza, A.2    Pérez, R.3    Martinez, P.4
  • 31
    • 0001522035 scopus 로고
    • Mapping minerals with imaging spectroscopy, U.S. Geological Survey
    • R. N. Clark, G. A. Swayze, and A. Gallagher, Mapping minerals with imaging spectroscopy, U.S. Geological Survey, Office Mineral Resour. Bull., vol. 2039, pp. 141-150, 1993
    • (1993) Office Mineral Resour. Bull , vol.2039 , pp. 141-150
    • Clark, R.N.1    Swayze, G.A.2    Gallagher, A.3
  • 32
    • 0001153986 scopus 로고
    • Simulation of truncated normal variables
    • Jun
    • C. P. Robert, Simulation of truncated normal variables, Statist. Comput., vol. 5, no. 2, pp. 121-125, Jun. 1995
    • (1995) Statist. Comput , vol.5 , Issue.2 , pp. 121-125
    • Robert, C.P.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.