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Volumn , Issue , 2008, Pages 3433-3436

Bayesian linear unmixing of hyperspectral images corrupted by colored Gaussian noise with unknown covariance matrix

Author keywords

Bayesian inference; Hyperspectral images; Monte Carlo methods; Spectral unmixing

Indexed keywords

ACOUSTICS; ERROR ANALYSIS; GAUSSIAN NOISE (ELECTRONIC); IMAGE ENHANCEMENT; SIGNAL PROCESSING; SPEECH; SQUARE WAVE GENERATORS; TARGET TRACKING; TRELLIS CODES;

EID: 51449083209     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2008.4518389     Document Type: Conference Paper
Times cited : (11)

References (10)
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    • Winter, M.E.1
  • 3
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    • Least squares subspace projection approach to mixed pixel classification for hyperspectral images
    • May
    • C.-I Chang, X.-L. Zhao, M. L. G. Althouse, and J. J. Pan, "Least squares subspace projection approach to mixed pixel classification for hyperspectral images," IEEE Trans. Geosci. and Remote Sensing, vol. 36, no. 3, pp. 898-912, May 1998.
    • (1998) IEEE Trans. Geosci. and Remote Sensing , vol.36 , Issue.3 , pp. 898-912
    • Chang, C.-I.1    Zhao, X.-L.2    Althouse, M.L.G.3    Pan, J.J.4
  • 4
    • 0035392132 scopus 로고    scopus 로고
    • Hyperspectral subpixel target detection using the linear mixing model
    • July
    • D. Manolakis, C. Siracusa, and G. Shaw, "Hyperspectral subpixel target detection using the linear mixing model," IEEE Trans. Geosci. and Remote Sensing, vol. 39, no. 7, pp. 1392-1409, July 2001.
    • (2001) IEEE Trans. Geosci. and Remote Sensing , vol.39 , Issue.7 , pp. 1392-1409
    • Manolakis, D.1    Siracusa, C.2    Shaw, G.3
  • 5
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    • Estimation of number of spectrally distinct signal sources in hyperspectral imagery
    • March
    • C.-I Chang and Q. Du, "Estimation of number of spectrally distinct signal sources in hyperspectral imagery," IEEE Trans. Geosci. and Remote Sensing, vol. 42, no. 3, pp. 608-619, March 2004.
    • (2004) IEEE Trans. Geosci. and Remote Sensing , vol.42 , Issue.3 , pp. 608-619
    • Chang, C.-I.1    Du, Q.2
  • 6
    • 34547519726 scopus 로고    scopus 로고
    • Spectral unmixing of hyperspectral images using a hierarchical Bayesian model
    • Honolulu, Hawaii, USA, April
    • N. Dobigeon and J.-Y. Tourneret, "Spectral unmixing of hyperspectral images using a hierarchical Bayesian model," in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), vol. 3, Honolulu, Hawaii, USA, April 2007, pp. 1209-1212.
    • (2007) Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP) , vol.3 , pp. 1209-1212
    • Dobigeon, N.1    Tourneret, J.-Y.2
  • 8
    • 34547501725 scopus 로고    scopus 로고
    • RSI Research Systems Inc, Boulder, CO USA, Sept
    • RSI (Research Systems Inc.), ENVI User's guide Version 4.0, Boulder, CO 80301 USA, Sept. 2003.
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  • 9
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    • Mixture models for anomaly detection in hyperspectral imagery
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    • C. J. Willis, "Mixture models for anomaly detection in hyperspectral imagery," in Military Remote Sensing, G. W. Kamerman and D. V. Willetts, Eds., vol. 5613, no. 1. SPIE, Dec. 2004, pp. 119-128.
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    • Willis, C.J.1
  • 10
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    • Target detection in hyperspectral images based on multi-component statistical models for representation of background clutter
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    • I. Kasen, P. E. Goa, and T. Skauli, "Target detection in hyperspectral images based on multi-component statistical models for representation of background clutter," in Electro-Optical and Infrared Systems: Technology and Applications, R. G. Driggers and D. A. Huckridge, Eds., vol. 5612, no. 1. SPIE, Dec. 2004, pp. 258-264.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.