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Volumn 53, Issue 3, 2015, Pages 1346-1354

A sparse representation-based binary hypothesis model for target detection in hyperspectral images

Author keywords

Binary hypothesis; Hyperspectral imagery; Sparse representation; Target detection

Indexed keywords

BINARY HYPOTHESIS; HYPER-SPECTRAL IMAGERIES; HYPER-SPECTRAL IMAGES; SPARSE REPRESENTATION;

EID: 84907464023     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2014.2337883     Document Type: Article
Times cited : (191)

References (32)
  • 2
    • 0035392132 scopus 로고    scopus 로고
    • Hyperspectral subpixel target detection using the linear mixing model
    • Jul.
    • 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.
    • (2001) IEEE Trans. Geosci. Remote Sens. , vol.39 , Issue.7 , pp. 1392-1409
    • Manolakis, D.1    Siracusa, C.2    Shaw, G.3
  • 3
    • 0036564131 scopus 로고    scopus 로고
    • Spectral imaging system analytical model for subpixel object detection
    • May.
    • J. P. Kerekes and J. E. Baum"Spectral imaging system analytical model for subpixel object detection" IEEE Trans. Geosci. Remote Sens., vol. 40, no. 5, pp. 1088-1101, May 2002.
    • (2002) IEEE Trans. Geosci. Remote Sens. , vol.40 , Issue.5 , pp. 1088-1101
    • Kerekes, J.P.1    Baum, J.E.2
  • 4
    • 80053006801 scopus 로고    scopus 로고
    • Image-derived prediction of spectral image utility for target detection applications
    • Apr.
    • M. S. Stefanou and J. P. Kerekes"Image-derived prediction of spectral image utility for target detection applications" IEEE Trans. Geosci. Remote Sens., vol. 48, no. 4, pp. 1827-1833, Apr. 2010.
    • (2010) IEEE Trans. Geosci. Remote Sens. , vol.48 , Issue.4 , pp. 1827-1833
    • Stefanou, M.S.1    Kerekes, J.P.2
  • 5
    • 85032751277 scopus 로고    scopus 로고
    • Detection algorithms for hyperspectral imaging applications
    • Jan.
    • D.Manolakis and G. Shaw"Detection algorithms for hyperspectral imaging applications" IEEE Signal Process. Mag., vol. 19, no. 1, pp. 29-43, Jan. 2002.
    • (2002) IEEE Signal Process. Mag. , vol.19 , Issue.1 , pp. 29-43
    • Manolakis, D.1    Shaw, G.2
  • 6
    • 77955682014 scopus 로고    scopus 로고
    • A tutorial overview of anomaly detection in hyperspectral images
    • Jul.
    • S. Matteoli, M. Diani, and G. Corsini"A tutorial overview of anomaly detection in hyperspectral images" IEEE Aerosp. Electron. Syst. Mag., vol. 25, no. 7, pp. 5-28, Jul. 2010.
    • (2010) IEEE Aerosp. Electron. Syst. Mag. , vol.25 , Issue.7 , pp. 5-28
    • Matteoli, S.1    Diani, M.2    Corsini, G.3
  • 7
    • 0033685691 scopus 로고    scopus 로고
    • Comparative analysis of hyperspectral adaptive matched filter detectors
    • Hyperspectr., Ultraspectr. Imagery 6, Apr.
    • D. Manolakis, G. Shaw, and N. Keshava"Comparative analysis of hyperspectral adaptive matched filter detectors" in Proc. SPIE Conf. Algorithms Multispectr., Hyperspectr., Ultraspectr. Imagery 6, Apr. 2000, vol. 4049, pp. 2-17.
    • (2000) Proc. SPIE Conf. Algorithms Multispectr. , vol.4049 , pp. 2-17
    • Manolakis, D.1    Shaw, G.2    Keshava, N.3
  • 8
    • 66249142802 scopus 로고    scopus 로고
    • Regularized spectral matched filter for target recognition in hyperspectral imagery
    • N. M. Nasrabadi"Regularized spectral matched filter for target recognition in hyperspectral imagery" IEEE Signal Process. Lett., vol. 15, pp. 317-320, 2008.
    • (2008) IEEE Signal Process. Lett. , vol.15 , pp. 317-320
    • Nasrabadi, N.M.1
  • 10
    • 17644371466 scopus 로고    scopus 로고
    • Hyperspectral image processing for automatic target detection applications
    • D. Manolakis, D. Marden, and G. A. Shaw"Hyperspectral image processing for automatic target detection applications" J. Lincoln Lab., vol. 14, no. 1, pp. 79-116, 2003.
    • (2003) J. Lincoln Lab. , vol.14 , Issue.1 , pp. 79-116
    • Manolakis, D.1    Marden, D.2    Shaw, G.A.3
  • 12
    • 65349171459 scopus 로고    scopus 로고
    • Sparse representation for classification of tumors using gene expression data
    • X. Hang and F.-X.Wu"Sparse representation for classification of tumors using gene expression data" J. Biomed. Biotechnol., vol. 2009, pp. 1-6, 2009.
    • (2009) J. Biomed. Biotechnol. , vol.2009 , pp. 1-6
    • Hang, X.1    Wu, F.-X.2
  • 13
    • 84871731919 scopus 로고    scopus 로고
    • Hyperspectral image classification via kernel sparse representation
    • Jan.
    • Y. Chen, N. M. Nasrabadi, and T. D. Tran"Hyperspectral image classification via kernel sparse representation" IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 217-231, Jan. 2013.
    • (2013) IEEE Trans. Geosci. Remote Sens. , vol.51 , Issue.1 , pp. 217-231
    • Chen, Y.1    Nasrabadi, N.M.2    Tran, T.D.3
  • 14
    • 84870541423 scopus 로고    scopus 로고
    • Exploiting sparsity in hyperspectral image classification via graphical models
    • May.
    • U. Srinivas, Y. Chen, V. Monga, N. M. Nasrabadi, and T. D. Tran"Exploiting sparsity in hyperspectral image classification via graphical models" IEEE Geosci. Remote Sens. Lett., vol. 10, no. 3, pp. 505-509, May 2013.
    • (2013) IEEE Geosci. Remote Sens. Lett. , vol.10 , Issue.3 , pp. 505-509
    • Srinivas, U.1    Chen, Y.2    Monga, V.3    Nasrabadi, N.M.4    Tran, T.D.5
  • 15
    • 69949128613 scopus 로고    scopus 로고
    • L1 unmixing and its application to hyperspectral image enhancement
    • Hyperspectr., Ultraspectr. Imagery 15, Apr.
    • Z. Guo, T. Wittman, and S. Osher"L1 unmixing and its application to hyperspectral image enhancement" in Proc. SPIE Conf. Algorithms Technol. Multispectr., Hyperspectr., Ultraspectr. Imagery 15, Apr. 2009, vol. 7334, pp. 1-9.
    • (2009) Proc. SPIE Conf. Algorithms Technol. Multispectr. , vol.7334 , pp. 1-9
    • Guo, Z.1    Wittman, T.2    Osher, S.3
  • 16
    • 84863011302 scopus 로고    scopus 로고
    • Sparse representation or collaborative representation: Which helps face recognition?
    • L. Zhang, M. Yang, and X. Feng"Sparse representation or collaborative representation: Which helps face recognition?" in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 471-478.
    • (2011) Proc. IEEE Int. Conf. Comput. Vis. , pp. 471-478
    • Zhang, L.1    Yang, M.2    Feng, X.3
  • 18
    • 84867068830 scopus 로고    scopus 로고
    • Sparse unsupervised dimensionality reduction for multiple view data
    • Oct.
    • Y. Han, F. Wu, D. Tao, and J. shao"Sparse unsupervised dimensionality reduction for multiple view data" IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 10, pp. 1485-1496, Oct. 2012.
    • (2012) IEEE Trans. Circuits Syst. Video Technol. , vol.22 , Issue.10 , pp. 1485-1496
    • Han, Y.1    Wu, F.2    Tao, D.3    Shao, J.4
  • 19
    • 84891011267 scopus 로고    scopus 로고
    • Sparse transfer manifold embedding for hyperspectral target detection
    • Mar.
    • L. Zhang, L. Zhang, D. Tao, and X. Huang"Sparse transfer manifold embedding for hyperspectral target detection" IEEE Trans. Geosci. Remote Sens., vol. 52, no. 2, pp. 1030-1043, Mar. 2014.
    • (2014) IEEE Trans. Geosci. Remote Sens. , vol.52 , Issue.2 , pp. 1030-1043
    • Zhang, L.1    Zhang, L.2    Tao, D.3    Huang, X.4
  • 20
    • 79957470922 scopus 로고    scopus 로고
    • Sparse representation for target detection in hyperspectral imagery
    • Jun.
    • Y. Chen, N. M. Nasrabadi, and T. D. Tran"Sparse representation for target detection in hyperspectral imagery" IEEE J. Sel. Topics Signal Process., vol. 5, no. 3, pp. 629-640, Jun. 2011.
    • (2011) IEEE J. Sel. Topics Signal Process. , vol.5 , Issue.3 , pp. 629-640
    • Chen, Y.1    Nasrabadi, N.M.2    Tran, T.D.3
  • 21
    • 79959708449 scopus 로고    scopus 로고
    • Simultaneous joint sparsity model for target detection in hyperspectral imagery
    • Jul.
    • Y. Chen, N. M. Nasrabadi, and T. D. Tran"Simultaneous joint sparsity model for target detection in hyperspectral imagery" IEEE Geosci. Remote Sens. Lett., vol. 8, no. 4, pp. 676-680, Jul. 2011.
    • (2011) IEEE Geosci. Remote Sens. Lett. , vol.8 , Issue.4 , pp. 676-680
    • Chen, Y.1    Nasrabadi, N.M.2    Tran, T.D.3
  • 22
    • 84873173002 scopus 로고    scopus 로고
    • Kernel sparse representation for hyperspectral target detection
    • Y. Chen, N. M. Nasrabadi, and T. D. Tran"Kernel sparse representation for hyperspectral target detection" in Proc. IEEE IGARSS, 2012, pp. 7484-7487.
    • (2012) Proc. IEEE IGARSS , pp. 7484-7487
    • Chen, Y.1    Nasrabadi, N.M.2    Tran, T.D.3
  • 24
    • 0033225639 scopus 로고    scopus 로고
    • Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions
    • Jun.
    • G. Healey and D. Slater"Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions" IEEE Trans. Geosci. Remote Sens., vol. 37, no. 6, pp. 2706-2717, Jun. 1999.
    • (1999) IEEE Trans. Geosci. Remote Sens. , vol.37 , Issue.6 , pp. 2706-2717
    • Healey, G.1    Slater, D.2
  • 25
    • 0036508022 scopus 로고    scopus 로고
    • Invariant subpixel material detection in hyperspectral imagery
    • Mar.
    • B. Thai and G. Healey"Invariant subpixel material detection in hyperspectral imagery" IEEE Trans. Geosci. Remote Sens., vol. 40, no. 3, pp. 599-608, Mar. 2002.
    • (2002) IEEE Trans. Geosci. Remote Sens. , vol.40 , Issue.3 , pp. 599-608
    • Thai, B.1    Healey, G.2
  • 26
    • 77952743135 scopus 로고    scopus 로고
    • Computational methods for sparse solution of linear inverse problems
    • Jun.
    • J. A. Tropp and S. J. Wright"Computational methods for sparse solution of linear inverse problems" Proc. IEEE, vol. 98, no. 6, pp. 948-958, Jun. 2010.
    • (2010) Proc. IEEE , vol.98 , Issue.6 , pp. 948-958
    • Tropp, J.A.1    Wright, S.J.2
  • 27
    • 64649083745 scopus 로고    scopus 로고
    • Signal recovery from random measurements via orthogonal matching pursuit
    • Dec.
    • J. A. Tropp and A. C. Gilbert"Signal recovery from random measurements via orthogonal matching pursuit" IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007.
    • (2007) IEEE Trans. Inf. Theory , vol.53 , Issue.12 , pp. 4655-4666
    • Tropp, J.A.1    Gilbert, A.C.2
  • 28
    • 0344082849 scopus 로고    scopus 로고
    • Dual-window-based anomaly detection for hyperspectral imagery
    • H. Kwon, S. Z. Der, and N. M. Nasrabadi"Dual-window-based anomaly detection for hyperspectral imagery" in Proc. SPIE, 2003, vol. 5094, pp. 148-158.
    • (2003) Proc. SPIE , vol.5094 , pp. 148-158
    • Kwon, H.1    Der, S.Z.2    Nasrabadi, N.M.3
  • 29
    • 33746885881 scopus 로고    scopus 로고
    • A support vector method for anomaly detection in hyperspectral imagery
    • Aug.
    • A. Banerjee, P. Burlina, and C. Diehl"A support vector method for anomaly detection in hyperspectral imagery" IEEE Trans. Geosci. Remote Sens., vol. 44, no. 8, pp. 2282-2291, Aug. 2006.
    • (2006) IEEE Trans. Geosci. Remote Sens. , vol.44 , Issue.8 , pp. 2282-2291
    • Banerjee, A.1    Burlina, P.2    Diehl, C.3
  • 30
    • 67349194302 scopus 로고    scopus 로고
    • A method for assessing spectral image utility
    • Jun.
    • M. S. Stefanou and J. P. Kerekes"A method for assessing spectral image utility" IEEE Trans. Geosci. Remote Sens., vol. 47, no. 6, pp. 1698-1706, Jun. 2009.
    • (2009) IEEE Trans. Geosci. Remote Sens. , vol.47 , Issue.6 , pp. 1698-1706
    • Stefanou, M.S.1    Kerekes, J.P.2
  • 31
    • 0035309562 scopus 로고    scopus 로고
    • Efficient detection in hyperspectral imagery
    • Apr.
    • S. M. Schweizer and J. M. F. Moura"Efficient detection in hyperspectral imagery" IEEE Trans. Image Process., vol. 10, no. 4, pp. 584-597, Apr. 2001.
    • (2001) IEEE Trans. Image Process. , vol.10 , Issue.4 , pp. 584-597
    • Schweizer, S.M.1    Moura, J.M.F.2
  • 32
    • 33846220041 scopus 로고    scopus 로고
    • A comparative analysis of kernel subspace target detectors for hyperspectral imagery
    • Jan.
    • H. Kwon and N. M. Nasrabadi"A comparative analysis of kernel subspace target detectors for hyperspectral imagery" EURASIP J. Appl. Signal Process., vol. 2007, no. 1, pp. 193-193, Jan. 2007.
    • (2007) EURASIP J. Appl. Signal Process. , vol.2007 , Issue.1 , pp. 193-193
    • Kwon, H.1    Nasrabadi, N.M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.