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Volumn 49, Issue 2, 2011, Pages 672-678

Second moment linear dimensionality as an alternative to virtual dimensionality

Author keywords

Covariance matrix; effective dimensionality; eigenvalues; hyperspectral imagery; virtual dimensionality (VD)

Indexed keywords

ARTIFICIAL DATA; EFFECTIVE DIMENSIONALITY; EIGENVALUES; GEOSCIENCES; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGERY; LINEAR DIMENSIONALITY; SECOND MOMENTS; VIRTUAL DIMENSIONALITY;

EID: 79151484627     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2010.2057434     Document Type: Article
Times cited : (33)

References (31)
  • 1
    • 0009055256 scopus 로고
    • Determining the number and identity of spectral endmembers: An integrated approach using Neyman-Pearson eigenthresholding and iterative constrained RMS error minimization
    • Feb
    • J. Harsanyi, W. Farrand, and C.-I. Chang, "Determining the number and identity of spectral endmembers: An integrated approach using Neyman-Pearson eigenthresholding and iterative constrained RMS error minimization", in Proc. 9th Thematic Conf. Geologic Remote Sens., Feb. 1993.
    • (1993) Proc. 9th Thematic Conf. Geologic Remote Sens.
    • Harsanyi, J.1    Farrand, W.2    Chang, C.-I.3
  • 2
    • 1842481516 scopus 로고    scopus 로고
    • Estimation of number of spectrally distinct signal sources in hyperspectral imagery
    • Mar
    • C.-I. Chang and Q. Du, "Estimation of number of spectrally distinct signal sources in hyperspectral imagery", IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, pp. 608-619, Mar. 2004.
    • (2004) IEEE Trans. Geosci. Remote Sens. , vol.42 , Issue.3 , pp. 608-619
    • Chang, C.-I.1    Du, Q.2
  • 3
    • 84887415911 scopus 로고    scopus 로고
    • A new growing method for simplex-based endmember extraction algorithm
    • Oct
    • C.-I. Chang, C.-C. Wu, W. Liu, and Y.-C. Ouyang, "A new growing method for simplex-based endmember extraction algorithm", IEEE Trans. Geosci. Remote Sens., vol. 44, pt. 1, no. 10, pp. 2804-2819, Oct. 2006.
    • (2006) IEEE Trans. Geosci. Remote Sens. , vol.44 , Issue.1-10 PART , pp. 2804-2819
    • Chang, C.-I.1    Wu, C.-C.2    Liu, W.3    Ouyang, Y.-C.4
  • 4
    • 33947653188 scopus 로고    scopus 로고
    • Automatic target recognition for hyperspectral imagery using high-order statistics
    • Oct
    • H. Ren, Q. Du, J. Wang, C.-I. Chang, J. O. Jensen, and J. L. Jensen, "Automatic target recognition for hyperspectral imagery using high-order statistics", IEEE Trans. Aerosp. Electron. Syst., vol. 42, no. 4, pp. 1372-1385, Oct. 2006.
    • (2006) IEEE Trans. Aerosp. Electron. Syst. , vol.42 , Issue.4 , pp. 1372-1385
    • Ren, H.1    Du, Q.2    Wang, J.3    Chang, C.-I.4    Jensen, J.O.5    Jensen, J.L.6
  • 5
    • 33750820859 scopus 로고    scopus 로고
    • Impact of initialization on design of endmember extraction algorithms
    • Nov
    • A. Plaza and C.-I. Chang, "Impact of initialization on design of endmember extraction algorithms", IEEE Trans. Geosci. Remote Sens., vol. 44, pt. 2, no. 11, pp. 3397-3407, Nov. 2006.
    • (2006) IEEE Trans. Geosci. Remote Sens. , vol.44 , Issue.2-11 PART , pp. 3397-3407
    • Plaza, A.1    Chang, C.-I.2
  • 6
    • 33744719449 scopus 로고    scopus 로고
    • Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis
    • Jun
    • J. Wang and C.-I. Chang, "Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis", IEEE Trans. Geosci. Remote Sens., vol. 44, no. 6, pp. 1586-1600, Jun. 2006.
    • (2006) IEEE Trans. Geosci. Remote Sens. , vol.44 , Issue.6 , pp. 1586-1600
    • Wang, J.1    Chang, C.-I.2
  • 7
    • 33744726231 scopus 로고    scopus 로고
    • Constrained band selection for hyperspectral imagery
    • Jun
    • C.-I. Chang and S. Wang, "Constrained band selection for hyperspectral imagery", IEEE Trans. Geosci. Remote Sens., vol. 44, no. 6, pp. 1575-1585, Jun. 2006.
    • (2006) IEEE Trans. Geosci. Remote Sens. , vol.44 , Issue.6 , pp. 1575-1585
    • Chang, C.-I.1    Wang, S.2
  • 8
    • 33748312145 scopus 로고    scopus 로고
    • Applications of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery
    • Sep
    • J. Wang and C.-I. Chang, "Applications of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery", IEEE Trans. Geosci. Remote Sens., vol. 44, no. 9, pp. 2601-2616, Sep. 2006.
    • (2006) IEEE Trans. Geosci. Remote Sens. , vol.44 , Issue.9 , pp. 2601-2616
    • Wang, J.1    Chang, C.-I.2
  • 9
    • 31144478389 scopus 로고    scopus 로고
    • A fast iterative algorithm for implementation of pixel purity index
    • Jan
    • C.-I. Chang and A. Plaza, "A fast iterative algorithm for implementation of pixel purity index", IEEE Geosci. Remote Sens. Lett., vol. 3, no. 1, pp. 63-67, Jan. 2006.
    • (2006) IEEE Geosci. Remote Sens. Lett. , vol.3 , Issue.1 , pp. 63-67
    • Chang, C.-I.1    Plaza, A.2
  • 10
    • 51849099605 scopus 로고    scopus 로고
    • Calculation of abundance factors in hyperspectral imaging using genetic algorithm
    • May 4-7
    • M. Farzam, S. Beheshti, and K. Raahemifar, "Calculation of abundance factors in hyperspectral imaging using genetic algorithm", in Proc. Can. Conf. Elect. Comput. Eng., May 4-7, 2008, pp. 837-842.
    • (2008) Proc. Can. Conf. Elect. Comput. Eng. , pp. 837-842
    • Farzam, M.1    Beheshti, S.2    Raahemifar, K.3
  • 12
    • 70349308345 scopus 로고    scopus 로고
    • Band selection and decision fusion for target detection in hyperspectral imagery
    • I. ul Haq and X. Xiaojian, "Band selection and decision fusion for target detection in hyperspectral imagery", in Proc. ICIEA, 2009, pp. 1468-1471.
    • (2009) Proc. ICIEA , pp. 1468-1471
    • Ul Haq, I.1    Xiaojian, X.2
  • 13
    • 70349194672 scopus 로고    scopus 로고
    • Band clustering and selection and decision fusion for target detection in hyperspectral imagery
    • I. ul Haq, X. Xiaojian, and A. Shahzad, "Band clustering and selection and decision fusion for target detection in hyperspectral imagery", in Proc. ICASSP, 2009, pp. 1101-1104.
    • (2009) Proc. ICASSP , pp. 1101-1104
    • Ul Haq, I.1    Xiaojian, X.2    Shahzad, A.3
  • 14
    • 67249128806 scopus 로고    scopus 로고
    • A new approach to band clustering and selection for hyperspectral imagery
    • I. ul Haq and X. Xiaojian, "A new approach to band clustering and selection for hyperspectral imagery", in Proc. ICSP, 2008, pp. 1198-1202.
    • (2008) Proc. ICSP , pp. 1198-1202
    • Ul Haq, I.1    Xiaojian, X.2
  • 15
    • 67651149491 scopus 로고    scopus 로고
    • Improved process for use of a simplex growing algorithm for endmember extraction
    • Jul
    • C.-C. Wu, C. S. Lo, and C.-I. Chang, "Improved process for use of a simplex growing algorithm for endmember extraction", IEEE Geosci. Remote Sens. Lett., vol. 6, no. 3, pp. 523-527, Jul. 2009.
    • (2009) IEEE Geosci. Remote Sens. Lett. , vol.6 , Issue.3 , pp. 523-527
    • Wu, C.-C.1    Lo, C.S.2    Chang, C.-I.3
  • 16
    • 72049087483 scopus 로고    scopus 로고
    • Component analysis-based unsupervised linear spectral mixture analysis for hyperspectral imagery
    • X. Jiao, Y. Du, and C.-I. Chang, "Component analysis-based unsupervised linear spectral mixture analysis for hyperspectral imagery", in Proc. WHISPERS, 2009, pp. 1-5.
    • (2009) Proc. WHISPERS , pp. 1-5
    • Jiao, X.1    Du, Y.2    Chang, C.-I.3
  • 17
    • 72049084762 scopus 로고    scopus 로고
    • Kernel-based linear spectral mixture analysis for hyperspectral image classification
    • K.-H. Liu, E. Wong, and C.-I. Chang, "Kernel-based linear spectral mixture analysis for hyperspectral image classification", in Proc. WHISPERS, 2009, pp. 1-4.
    • (2009) Proc. WHISPERS , pp. 1-4
    • Liu, K.-H.1    Wong, E.2    Chang, C.-I.3
  • 18
    • 51849099605 scopus 로고    scopus 로고
    • Calculation of abundance factors in hyperspectral imaging using genetic algorithm
    • M. Farzam, S. Beheshti, and K. Raahemifar, "Calculation of abundance factors in hyperspectral imaging using genetic algorithm", in Proc. CCECE, 2008, pp. 837-842.
    • (2008) Proc. CCECE , pp. 837-842
    • Farzam, M.1    Beheshti, S.2    Raahemifar, K.3
  • 19
    • 34247356156 scopus 로고    scopus 로고
    • Spatially coherent nonlinear dimensionality reduction and segmentation of hyperspectral images
    • Apr
    • A. Mohan, G. Sapiro, and E. Bosch, "Spatially coherent nonlinear dimensionality reduction and segmentation of hyperspectral images", IEEE Geosci. Remote Sens. Lett., vol. 4, no. 2, pp. 206-210, Apr. 2007.
    • (2007) IEEE Geosci. Remote Sens. Lett. , vol.4 , Issue.2 , pp. 206-210
    • Mohan, A.1    Sapiro, G.2    Bosch, E.3
  • 20
    • 50149118205 scopus 로고    scopus 로고
    • Probabilistic classification of hyperspectral images by learning nonlinear dimensionality reduction mapping
    • X. R. Wang, S. Kumar, F. Ramos, and T. Kaupp, "Probabilistic classification of hyperspectral images by learning nonlinear dimensionality reduction mapping", in Proc. 9th Int. Conf. Inf. Fusion, 2006, pp. 1-8.
    • (2006) Proc. 9th Int. Conf. Inf. Fusion , pp. 1-8
    • Wang, X.R.1    Kumar, S.2    Ramos, F.3    Kaupp, T.4
  • 21
    • 49749128335 scopus 로고    scopus 로고
    • A new nonlinear dimensionality reduction method with application to hyperspectral image analysis
    • S.-E. Qian and G. Chen, "A new nonlinear dimensionality reduction method with application to hyperspectral image analysis", in Proc. IEEE IGARSS, 2007, pp. 270-273.
    • (2007) Proc. IEEE IGARSS , pp. 270-273
    • Qian, S.-E.1    Chen, G.2
  • 27
    • 15944427807 scopus 로고    scopus 로고
    • Simplex projection methods for selection of endmembers in hyperspectral imagery
    • Anchorage, AK, Sep
    • P. Bajorski, "Simplex projection methods for selection of endmembers in hyperspectral imagery", in Proc. IEEE Int. Geosci. Remote Sens. Symp., Anchorage, AK, Sep. 2004, vol. 5, pp. 3207-3210.
    • (2004) Proc. IEEE Int. Geosci. Remote Sens. Symp. , vol.5 , pp. 3207-3210
    • Bajorski, P.1
  • 28
    • 27544471920 scopus 로고    scopus 로고
    • Stepwise simplex projection method for selection of endmembers in hyperspectral images
    • Orlando, FL, Mar./Apr
    • P. Bajorski, "Stepwise simplex projection method for selection of endmembers in hyperspectral images", in Proc. SPIE-Algorithms Technol. Multispectral, Hyperspectral, Ultraspectral Imagery XI, Orlando, FL, Mar./Apr. 2005, vol. 5806, pp. 318-329.
    • (2005) Proc. SPIE-Algorithms Technol. Multispectral, Hyperspectral, Ultraspectral Imagery XI , vol.5806 , pp. 318-329
    • Bajorski, P.1
  • 29
    • 34347209724 scopus 로고    scopus 로고
    • Analytical comparison of the matched filter and orthogonal subspace projection detectors for hyperspectral images
    • Jul
    • P. Bajorski, "Analytical comparison of the matched filter and orthogonal subspace projection detectors for hyperspectral images", IEEE Trans. Geosci. Remote Sens., vol. 45, pt. 2, no. 7, pp. 2394-2402, Jul. 2007.
    • (2007) IEEE Trans. Geosci. Remote Sens. , vol.45 , Issue.2-7 PART , pp. 2394-2402
    • Bajorski, P.1
  • 30
    • 7044247421 scopus 로고    scopus 로고
    • A new statistical model for Markovian classification of urban areas in high-resolution SAR images
    • Oct
    • C. Tison, J.-M. Nicolas, F. Tupin, and H. Maitre, "A new statistical model for Markovian classification of urban areas in high-resolution SAR images", IEEE Trans. Geosci. Remote Sens., vol. 42, no. 10, pp. 2046-2057, Oct. 2004.
    • (2004) IEEE Trans. Geosci. Remote Sens. , vol.42 , Issue.10 , pp. 2046-2057
    • Tison, C.1    Nicolas, J.-M.2    Tupin, F.3    Maitre, H.4
  • 31
    • 33646885204 scopus 로고    scopus 로고
    • SAR amplitude probability density function estimation based on a generalized Gaussian model
    • Jun
    • G. Moser, J. Zerubia, and S. B. Serpico, "SAR amplitude probability density function estimation based on a generalized Gaussian model", IEEE Trans. Image Process., vol. 15, no. 6, pp. 1429-1442, Jun. 2006.
    • (2006) IEEE Trans. Image Process. , vol.15 , Issue.6 , pp. 1429-1442
    • Moser, G.1    Zerubia, J.2    Serpico, S.B.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.