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Volumn 55, Issue 4, 2017, Pages 1967-1974

Tensor Matched Subspace Detector for Hyperspectral Target Detection

Author keywords

Hyperspectral imagery; subspace detector; target detection; tensor

Indexed keywords

DATA HANDLING; ITERATIVE METHODS; SPECTROSCOPY; TENSORS;

EID: 85008433303     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2016.2632863     Document Type: Article
Times cited : (98)

References (47)
  • 2
    • 85032751277 scopus 로고    scopus 로고
    • Detection algorithms for hyperspectral imaging applications
    • Jan.
    • D. Manolakis and G. S. 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.S.2
  • 3
    • 66249130979 scopus 로고    scopus 로고
    • Automated hyperspectral cueing for civilian search and rescue
    • Jun.
    • M. T. Eismann, A. D. Stocker, and N. M. Nasrabadi, "Automated hyperspectral cueing for civilian search and rescue, " Proc. IEEE, vol. 97, no. 6, pp. 1031-1055, Jun. 2009.
    • (2009) Proc. IEEE , vol.97 , Issue.6 , pp. 1031-1055
    • Eismann, M.T.1    Stocker, A.D.2    Nasrabadi, N.M.3
  • 4
    • 85032752318 scopus 로고    scopus 로고
    • Hyperspectral target detection: An overview of current and future challenges
    • Jan.
    • N. M. Nasrabadi, "Hyperspectral target detection: An overview of current and future challenges, " IEEE Signal Process. Mag., vol. 31, no. 1, pp. 34-44, Jan. 2014.
    • (2014) IEEE Signal Process. Mag. , vol.31 , Issue.1 , pp. 34-44
    • Nasrabadi, N.M.1
  • 5
    • 77956552496 scopus 로고    scopus 로고
    • Detection algorithms for hyperspectral imaging applications: A signal processing perspective
    • Oct.
    • D. Manolakis, "Detection algorithms for hyperspectral imaging applications: A signal processing perspective, " in Proc. IEEE Workshop Adv. Techn. Anal. Remotely Sensed Data, Oct. 2003, pp. 378-384.
    • (2003) Proc. IEEE Workshop Adv. Techn. Anal. Remotely Sensed Data , pp. 378-384
    • Manolakis, D.1
  • 6
    • 70350676140 scopus 로고    scopus 로고
    • Target detection with semisupervised kernel orthogonal subspace projection
    • Nov.
    • L. Capobianco, A. Garzelli, and G. Camps-Valls, "Target detection with semisupervised kernel orthogonal subspace projection, " IEEE Trans. Geosci. Remote Sens., vol. 47, no. 11, pp. 3822-3833, Nov. 2009.
    • (2009) IEEE Trans. Geosci. Remote Sens. , vol.47 , Issue.11 , pp. 3822-3833
    • Capobianco, L.1    Garzelli, A.2    Camps-Valls, G.3
  • 7
    • 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
  • 8
    • 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, " Lincoln Lab. J., vol. 14, no. 1, pp. 79-116, 2003.
    • (2003) Lincoln Lab. J. , vol.14 , Issue.1 , pp. 79-116
    • Manolakis, D.1    Marden, D.2    Shaw, G.A.3
  • 9
    • 85032750872 scopus 로고    scopus 로고
    • Detection algorithms in hyperspectral imaging systems: An overview of practical algorithms
    • Jan.
    • D. Manolakis, E. Truslow, M. Pieper, T. Cooley, and M. Brueggeman, "Detection algorithms in hyperspectral imaging systems: An overview of practical algorithms, " IEEE Signal Process. Mag., vol. 31, no. 1, pp. 24-33, Jan. 2014.
    • (2014) IEEE Signal Process. Mag. , vol.31 , Issue.1 , pp. 24-33
    • Manolakis, D.1    Truslow, E.2    Pieper, M.3    Cooley, T.4    Brueggeman, M.5
  • 12
    • 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
  • 13
    • 0035333298 scopus 로고    scopus 로고
    • Distributed signal detection under the Neyman-Pearson criterion
    • May
    • Q. Yan and R. S. Blum, "Distributed signal detection under the Neyman-Pearson criterion, " IEEE Trans. Inf. Theory, vol. 47, no. 4, pp. 1368-1377, May 2001.
    • (2001) IEEE Trans. Inf. Theory , vol.47 , Issue.4 , pp. 1368-1377
    • Yan, Q.1    Blum, R.S.2
  • 15
    • 0028467206 scopus 로고
    • Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach
    • Jul.
    • J. C. Harsanyi and C.-I. Chang, "Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach, " IEEE Trans. Geosci. Remote Sens., vol. 32, no. 4, pp. 779-785, Jul. 1994.
    • (1994) IEEE Trans. Geosci. Remote Sens. , vol.32 , Issue.4 , pp. 779-785
    • Harsanyi, J.C.1    Chang, C.-I.2
  • 16
    • 0028485933 scopus 로고
    • Matched subspace detectors
    • Aug.
    • L. L. Scharf and B. Friedlander, "Matched subspace detectors, " IEEE Trans. Signal Process., vol. 42, no. 8, pp. 2146-2157, Aug. 1994.
    • (1994) IEEE Trans. Signal Process. , vol.42 , Issue.8 , pp. 2146-2157
    • Scharf, L.L.1    Friedlander, B.2
  • 18
    • 85027923663 scopus 로고    scopus 로고
    • Nonrigid structure from motion via sparse representation
    • Aug.
    • K. Li, J. Yang, and J. Jiang, "Nonrigid structure from motion via sparse representation, " IEEE Trans. Cybern., vol. 45, no. 8, pp. 1401-1413, Aug. 2015.
    • (2015) IEEE Trans. Cybern. , vol.45 , Issue.8 , pp. 1401-1413
    • Li, K.1    Yang, J.2    Jiang, J.3
  • 19
    • 84937019908 scopus 로고    scopus 로고
    • Sparse representation denoising framework for 3-D building reconstruction from airborne LiDAR data
    • May
    • Z. Cao and Y. Gu, "Sparse representation denoising framework for 3-D building reconstruction from airborne LiDAR data, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 5, pp. 1888-1900, May 2016, doi: 10. 1109/JSTARS. 2015. 2448126.
    • (2016) IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol.9 , Issue.5 , pp. 1888-1900
    • Cao, Z.1    Gu, Y.2
  • 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
    • 38749143022 scopus 로고    scopus 로고
    • MPCA: Multilinear principal component analysis of tensor objects
    • Jan.
    • H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, "MPCA: Multilinear principal component analysis of tensor objects, " IEEE Trans. Neural Netw., vol. 19, no. 1, pp. 18-39, Jan. 2008.
    • (2008) IEEE Trans. Neural Netw. , vol.19 , Issue.1 , pp. 18-39
    • Lu, H.1    Plataniotis, K.N.2    Venetsanopoulos, A.N.3
  • 22
    • 33646866110 scopus 로고    scopus 로고
    • An efficient content-adaptive motioncompensated 3-D DWT with enhanced spatial and temporal scalability
    • Jun.
    • N. Mehrseresht and D. Taubman, "An efficient content-adaptive motioncompensated 3-D DWT with enhanced spatial and temporal scalability, " IEEE Trans. Image Process., vol. 15, no. 6, pp. 1397-1412, Jun. 2006.
    • (2006) IEEE Trans. Image Process. , vol.15 , Issue.6 , pp. 1397-1412
    • Mehrseresht, N.1    Taubman, D.2
  • 23
    • 80053571096 scopus 로고    scopus 로고
    • Hyperspectral image classification using dictionary-based sparse representation
    • Oct.
    • Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Hyperspectral image classification using dictionary-based sparse representation, " IEEE Trans. Geosci. Remote Sens., vol. 49, no. 10, pp. 3973-3985, Oct. 2011.
    • (2011) IEEE Trans. Geosci. Remote Sens. , vol.49 , Issue.10 , pp. 3973-3985
    • Chen, Y.1    Nasrabadi, N.M.2    Tran, T.D.3
  • 24
    • 48849090040 scopus 로고    scopus 로고
    • Improvement of target detection methods by multiway filtering
    • Aug.
    • N. Renard and S. Bourennane, "Improvement of target detection methods by multiway filtering, " IEEE Trans. Geosci. Remote Sens., vol. 46, no. 8, pp. 2407-2417, Aug. 2008.
    • (2008) IEEE Trans. Geosci. Remote Sens. , vol.46 , Issue.8 , pp. 2407-2417
    • Renard, N.1    Bourennane, S.2
  • 25
    • 79953195466 scopus 로고    scopus 로고
    • Improvement of targetdetection algorithms based on adaptive three-dimensional filtering
    • Apr.
    • S. Bourennane, C. Fossati, and A. Cailly, "Improvement of targetdetection algorithms based on adaptive three-dimensional filtering, " IEEE Trans. Geosci. Remote Sens., vol. 49, no. 4, pp. 1383-1395, Apr. 2011.
    • (2011) IEEE Trans. Geosci. Remote Sens. , vol.49 , Issue.4 , pp. 1383-1395
    • Bourennane, S.1    Fossati, C.2    Cailly, A.3
  • 26
    • 84880365414 scopus 로고    scopus 로고
    • Hyperspectral image noise reduction based on rank-1 tensor decomposition
    • Sep.
    • X. Guo, X. Huang, L. Zhang, and L. Zhang, "Hyperspectral image noise reduction based on rank-1 tensor decomposition, " ISPRS J. Photogram. Remote Sens., vol. 83, pp. 50-63, Sep. 2013.
    • (2013) ISPRS J. Photogram. Remote Sens. , vol.83 , pp. 50-63
    • Guo, X.1    Huang, X.2    Zhang, L.3    Zhang, L.4
  • 27
    • 63149112633 scopus 로고    scopus 로고
    • Dimensionality reduction based on tensor modeling for classification methods
    • Apr.
    • N. Renard and S. Bourennane, "Dimensionality reduction based on tensor modeling for classification methods, " IEEE Trans. Geosci. Remote Sens., vol. 47, no. 4, pp. 1123-1131, Apr. 2009.
    • (2009) IEEE Trans. Geosci. Remote Sens. , vol.47 , Issue.4 , pp. 1123-1131
    • Renard, N.1    Bourennane, S.2
  • 28
    • 77958020335 scopus 로고    scopus 로고
    • Improvement of classification for hyperspectral images based on tensor modeling
    • Oct.
    • S. Bourennane, C. Fossati, and A. Cailly, "Improvement of classification for hyperspectral images based on tensor modeling, " IEEE Geosci. Remote Sens. Lett., vol. 7, no. 4, pp. 801-805, Oct. 2010.
    • (2010) IEEE Geosci. Remote Sens. Lett. , vol.7 , Issue.4 , pp. 801-805
    • Bourennane, S.1    Fossati, C.2    Cailly, A.3
  • 29
    • 84871736244 scopus 로고    scopus 로고
    • Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction
    • Jan.
    • L. Zhang, L. Zhang, D. Tao, and X. Huang, "Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction, " IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 242-256, Jan. 2013.
    • (2013) IEEE Trans. Geosci. Remote Sens. , vol.51 , Issue.1 , pp. 242-256
    • Zhang, L.1    Zhang, L.2    Tao, D.3    Huang, X.4
  • 30
    • 79952027705 scopus 로고    scopus 로고
    • A multifeature tensor for remote-sensing target recognition
    • Mar.
    • L. Zhang, L. Zhang, D. Tao, and X. Huang, "A multifeature tensor for remote-sensing target recognition, " IEEE Geosci. Remote Sens. Lett., vol. 8, no. 2, pp. 374-378, Mar. 2011.
    • (2011) IEEE Geosci. Remote Sens. Lett. , vol.8 , Issue.2 , pp. 374-378
    • Zhang, L.1    Zhang, L.2    Tao, D.3    Huang, X.4
  • 31
    • 85027953375 scopus 로고    scopus 로고
    • Discriminant tensor spectral-spatial feature extraction for hyperspectral image classification
    • May
    • Z. Zhonget al.,"Discriminant tensor spectral-spatial feature extraction for hyperspectral image classification, " IEEE Geosci. Remote Sens. Lett., vol. 12, no. 5, pp. 1028-1032, May 2015.
    • (2015) IEEE Geosci. Remote Sens. Lett. , vol.12 , Issue.5 , pp. 1028-1032
    • Zhong, Z.1
  • 32
    • 84867399355 scopus 로고    scopus 로고
    • Classification of hyperspectral images by tensor modeling and additive morphological decomposition
    • Feb.
    • S. Velasco-Forero and J. Angulo, "Classification of hyperspectral images by tensor modeling and additive morphological decomposition, " Pattern Recognit., vol. 46, no. 2, pp. 566-577, Feb. 2013.
    • (2013) Pattern Recognit. , vol.46 , Issue.2 , pp. 566-577
    • Velasco-Forero, S.1    Angulo, J.2
  • 33
    • 38149116161 scopus 로고    scopus 로고
    • Improvement of classification using a joint spectral dimensionality reduction and lower rank spatial approximation for hyperspectral images
    • Aug.
    • N. Renard, S. Bourennane, and J. Blanc-Talon, "Improvement of classification using a joint spectral dimensionality reduction and lower rank spatial approximation for hyperspectral images, " in Proc. Int. Conf. Adv. Concepts Intell. Vis. Syst. (ACIVS), vol. 4678. Aug. 2007, pp. 132-143.
    • (2007) Proc. Int. Conf. Adv. Concepts Intell. Vis. Syst. (ACIVS) , vol.4678 , pp. 132-143
    • Renard, N.1    Bourennane, S.2    Blanc-Talon, J.3
  • 34
    • 55349111484 scopus 로고    scopus 로고
    • Generalised-structure-tensor-based infrared small target detection
    • Nov.
    • C. Gao, J. Tian, and P. Wang, "Generalised-structure-tensor-based infrared small target detection, " Electron. Lett., vol. 44, no. 23, pp. 1349-1351, Nov. 2008.
    • (2008) Electron. Lett. , vol.44 , Issue.23 , pp. 1349-1351
    • Gao, C.1    Tian, J.2    Wang, P.3
  • 36
    • 84928163552 scopus 로고    scopus 로고
    • A high-order statistical tensor based algorithm for anomaly detection in hyperspectral imagery
    • Nov.
    • X. Geng, K. Sun, L. Ji, and Y. Zhao, "A high-order statistical tensor based algorithm for anomaly detection in hyperspectral imagery, " Sci. Rep., vol. 4, p. 6869, Nov. 2014.
    • (2014) Sci. Rep. , vol.4 , pp. 6869
    • Geng, X.1    Sun, K.2    Ji, L.3    Zhao, Y.4
  • 37
    • 84863807054 scopus 로고    scopus 로고
    • Improvement of target detection based on tensorial modelling
    • Aalborg, Denmark, Aug.
    • S. Bourennane, C. Fossati, and A. Cailly, "Improvement of target detection based on tensorial modelling, " in Proc. Eur. Signal Process. Conf. (EUSIPCO), Aalborg, Denmark, Aug. 2010, pp. 304-308.
    • (2010) Proc. Eur. Signal Process. Conf. (EUSIPCO) , pp. 304-308
    • Bourennane, S.1    Fossati, C.2    Cailly, A.3
  • 39
    • 33644868367 scopus 로고    scopus 로고
    • Kernel matched subspace detectors for hyperspectral target detection
    • Feb.
    • H. Kwon and N. M. Nasrabadi, "Kernel matched subspace detectors for hyperspectral target detection, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 2, pp. 178-194, Feb. 2006.
    • (2006) IEEE Trans. Pattern Anal. Mach. Intell. , vol.28 , Issue.2 , pp. 178-194
    • Kwon, H.1    Nasrabadi, N.M.2
  • 41
    • 0345306717 scopus 로고    scopus 로고
    • Robust Gaussian and non-Gaussian matched subspace detection
    • Dec.
    • M. N. Desai and R. S. Mangoubi, "Robust Gaussian and non-Gaussian matched subspace detection, " IEEE Trans. Signal Process., vol. 51, no. 12, pp. 3115-3127, Dec. 2003.
    • (2003) IEEE Trans. Signal Process. , vol.51 , Issue.12 , pp. 3115-3127
    • Desai, M.N.1    Mangoubi, R.S.2
  • 42
    • 85032751899 scopus 로고    scopus 로고
    • Parallel randomly compressed cubes: A scalable distributed architecture for big tensor decomposition
    • Sep.
    • N. D. Sidiropoulos, E. E. Papalexakis, and C. Faloutsos, "Parallel randomly compressed cubes: A scalable distributed architecture for big tensor decomposition, " IEEE Signal Process. Mag., vol. 31, no. 5, pp. 57-70, Sep. 2014.
    • (2014) IEEE Signal Process. Mag. , vol.31 , Issue.5 , pp. 57-70
    • Sidiropoulos, N.D.1    Papalexakis, E.E.2    Faloutsos, C.3
  • 44
    • 68149125583 scopus 로고    scopus 로고
    • A Newton-Grassmann method for computing the best multilinear rank-(r1, r2, r3) approximation of a tensor
    • Mar.
    • L. Elden and B. Savas, "A Newton-Grassmann method for computing the best multilinear rank-(r1, r2, r3) approximation of a tensor, " SIAM J. Matrix Anal. Appl., vol. 31, no. 2, pp. 248-271, Mar. 2009.
    • (2009) SIAM J. Matrix Anal. Appl. , vol.31 , Issue.2 , pp. 248-271
    • Elden, L.1    Savas, B.2
  • 45
    • 84947037971 scopus 로고    scopus 로고
    • Hierarchical suppression method for hyperspectral target detection
    • Jan.
    • Z. Zou and Z. Shi, "Hierarchical suppression method for hyperspectral target detection, " IEEE Trans. Geosci. Remote Sens., vol. 54, no. 1, pp. 330-342, Jan. 2016.
    • (2016) IEEE Trans. Geosci. Remote Sens. , vol.54 , Issue.1 , pp. 330-342
    • Zou, Z.1    Shi, Z.2
  • 46
    • 84891011267 scopus 로고    scopus 로고
    • Sparse transfer manifold embedding for hyperspectral target detection
    • Feb.
    • 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, Feb. 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
  • 47
    • 84919790878 scopus 로고    scopus 로고
    • Compression of hyperspectral remote sensing images by tensor approach
    • Jan.
    • L. Zhang, L. Zhang, D. Tao, X. Huang, and B. Du, "Compression of hyperspectral remote sensing images by tensor approach, " Neurocomputing, vol. 147, pp. 358-363, Jan. 2015.
    • (2015) Neurocomputing , vol.147 , pp. 358-363
    • Zhang, L.1    Zhang, L.2    Tao, D.3    Huang, X.4    Du, B.5


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