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Volumn 151, Issue P1, 2015, Pages 175-187

Hyperspectral anomaly change detection with slow feature analysis

Author keywords

Anomaly change detection; Hyperion; Hyperspectral image; Multi temporal images; Slow feature analysis

Indexed keywords

FEATURE EXTRACTION; HYPERSPECTRAL IMAGING; IMAGE ENHANCEMENT; REMOTE SENSING; SPECTROSCOPY;

EID: 84922829753     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.09.058     Document Type: Article
Times cited : (35)

References (46)
  • 1
    • 0024855524 scopus 로고
    • Review article digital change detection techniques using remotely-sensed data
    • Singh A. Review article digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 1989, 10:989-1003.
    • (1989) Int. J. Remote Sens. , vol.10 , pp. 989-1003
    • Singh, A.1
  • 4
    • 67349132266 scopus 로고    scopus 로고
    • Remote sensing change detection tools for natural resource managers: understanding concepts and tradeoffs in the design of landscape monitoring projects
    • Kennedy R.E., Townsend P.A., Gross J.E., Cohen W.B., Bolstad P., Wang Y.Q., Adams P. Remote sensing change detection tools for natural resource managers: understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sens. Environ. 2009, 113:1382-1396.
    • (2009) Remote Sens. Environ. , vol.113 , pp. 1382-1396
    • Kennedy, R.E.1    Townsend, P.A.2    Gross, J.E.3    Cohen, W.B.4    Bolstad, P.5    Wang, Y.Q.6    Adams, P.7
  • 5
    • 84876726723 scopus 로고    scopus 로고
    • Change detection from remotely sensed images: from pixel-based to object-based approaches
    • Hussain M., Chen D., Cheng A., Wei H., Stanley D. Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J. Photogramm. 2013, 80:91-106.
    • (2013) ISPRS J. Photogramm. , vol.80 , pp. 91-106
    • Hussain, M.1    Chen, D.2    Cheng, A.3    Wei, H.4    Stanley, D.5
  • 6
    • 77955315839 scopus 로고    scopus 로고
    • Updating the 2001 national land cover database impervious surface products to 2006 using landsat imagery change detection methods
    • Xian G., Homer C. Updating the 2001 national land cover database impervious surface products to 2006 using landsat imagery change detection methods. Remote Sens. Environ. 2010, 114:1676-1686.
    • (2010) Remote Sens. Environ. , vol.114 , pp. 1676-1686
    • Xian, G.1    Homer, C.2
  • 7
    • 64549085340 scopus 로고    scopus 로고
    • Updating the 2001 national land cover database land cover classification to 2006 by using landsat imagery change detection methods
    • Xian G., Homer C., Fry J. Updating the 2001 national land cover database land cover classification to 2006 by using landsat imagery change detection methods. Remote Sens. Environ. 2009, 113:1133-1147.
    • (2009) Remote Sens. Environ. , vol.113 , pp. 1133-1147
    • Xian, G.1    Homer, C.2    Fry, J.3
  • 8
    • 84882843009 scopus 로고    scopus 로고
    • Unsupervised transfer learning for target detection from hyperspectral images
    • Du B., Zhang L., Tao D., Zhang D. Unsupervised transfer learning for target detection from hyperspectral images. Neurocomputing 2013, 120:72-82.
    • (2013) Neurocomputing , vol.120 , pp. 72-82
    • Du, B.1    Zhang, L.2    Tao, D.3    Zhang, D.4
  • 9
    • 84871736244 scopus 로고    scopus 로고
    • Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction
    • Zhang L., Zhang L., Tao D., Huang X. Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction. IEEE Trans. Geosci. Remote Sens 2013, 51:242-256.
    • (2013) IEEE Trans. Geosci. Remote Sens , vol.51 , pp. 242-256
    • Zhang, L.1    Zhang, L.2    Tao, D.3    Huang, X.4
  • 10
    • 85032751896 scopus 로고    scopus 로고
    • Hyperspectral image data analysis
    • Landgrebe D. Hyperspectral image data analysis. IEEE Signal Process. Mag. 2002, 19:17-28.
    • (2002) IEEE Signal Process. Mag. , vol.19 , pp. 17-28
    • Landgrebe, D.1
  • 11
    • 84863976875 scopus 로고    scopus 로고
    • A discriminative manifold learning based dimension reduction method
    • Du B., Zhang L., Zhang L., Chen T., Wu K. A discriminative manifold learning based dimension reduction method. Int. J. Fuzzy Syst. 2012, 14:272-277.
    • (2012) Int. J. Fuzzy Syst. , vol.14 , pp. 272-277
    • Du, B.1    Zhang, L.2    Zhang, L.3    Chen, T.4    Wu, K.5
  • 13
    • 44649202790 scopus 로고    scopus 로고
    • A new sub-pixel mapping algorithm based on a BP neural network with an observation model
    • Zhang L., Wu K., Zhong Y., Li P. A new sub-pixel mapping algorithm based on a BP neural network with an observation model. Neurocomputing 2008, 71:2046-2054.
    • (2008) Neurocomputing , vol.71 , pp. 2046-2054
    • Zhang, L.1    Wu, K.2    Zhong, Y.3    Li, P.4
  • 14
    • 75749152522 scopus 로고    scopus 로고
    • Object-oriented subspace analysis for airborne hyperspectral remote sensing imagery
    • Zhang L., Huang X. Object-oriented subspace analysis for airborne hyperspectral remote sensing imagery. Neurocomputing 2010, 73:927-936.
    • (2010) Neurocomputing , vol.73 , pp. 927-936
    • Zhang, L.1    Huang, X.2
  • 18
    • 84867072485 scopus 로고    scopus 로고
    • Application of model-based change detection to airborne vnir/swir hyperspectral imagery
    • Meola J., Eismann M.T., Moses R.L., Ash J.N. Application of model-based change detection to airborne vnir/swir hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 2012, 50:3693-3706.
    • (2012) IEEE Trans. Geosci. Remote Sens. , vol.50 , pp. 3693-3706
    • Meola, J.1    Eismann, M.T.2    Moses, R.L.3    Ash, J.N.4
  • 19
    • 84864282874 scopus 로고    scopus 로고
    • Local coregistration adjustment for anomalous change detection
    • Theiler J., Wohlberg B. Local coregistration adjustment for anomalous change detection. IEEE Trans. Geosci. Remote Sens. 2012, 50:3107-3116.
    • (2012) IEEE Trans. Geosci. Remote Sens. , vol.50 , pp. 3107-3116
    • Theiler, J.1    Wohlberg, B.2
  • 20
    • 85008024035 scopus 로고    scopus 로고
    • Hyperspectral change detection in the presence of diurnal and seasonal variations
    • Eismann M.T., Meola J., Hardie R.C. Hyperspectral change detection in the presence of diurnal and seasonal variations. IEEE Trans. Geosci. Remote Sens 2008, 46:237-249.
    • (2008) IEEE Trans. Geosci. Remote Sens , vol.46 , pp. 237-249
    • Eismann, M.T.1    Meola, J.2    Hardie, R.C.3
  • 22
    • 77951208379 scopus 로고    scopus 로고
    • Elliptically contoured distributions for anomalous change detection in hyperspectral imagery
    • Theiler J., Scovel C., Wohlberg B., Foy B.R. Elliptically contoured distributions for anomalous change detection in hyperspectral imagery. IEEE Geosci. Remote Sens. Lett. 2010, 7:271-275.
    • (2010) IEEE Geosci. Remote Sens. Lett. , vol.7 , pp. 271-275
    • Theiler, J.1    Scovel, C.2    Wohlberg, B.3    Foy, B.R.4
  • 23
    • 0005153149 scopus 로고    scopus 로고
    • Long-interval chronochrome target detection
    • Proc. 1997 International Symposium on Spectral Sensing Research
    • A. Schaum, A. Stocker, Long-interval chronochrome target detection, in: Proc. 1997 International Symposium on Spectral Sensing Research, 1998, pp. 1760-1770.
    • (1998) , pp. 1760-1770
    • Schaum, A.1    Stocker, A.2
  • 24
    • 84922833060 scopus 로고    scopus 로고
    • Hyperspectral Change Detection and Supervised Matched Filtering Based on Covariance Equalization, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X
    • Orlando, Florida, USA
    • A.P. Schaum, A. Stocker, Hyperspectral Change Detection and Supervised Matched Filtering Based on Covariance Equalization, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, Orlando, Florida, USA, 2004, pp. 77.
    • (2004) , pp. 77
    • Schaum, A.P.1    Stocker, A.2
  • 25
    • 0038444539 scopus 로고    scopus 로고
    • Object detection by using whitening/dewhitening to transform target signatures in multitemporal hyperspectral and multispectral imagery
    • Mayer R., Bucholtz F., Scribner D. Object detection by using whitening/dewhitening to transform target signatures in multitemporal hyperspectral and multispectral imagery. IEEE Trans. Geosci. Remote Sens 2003, 41:1136-1142.
    • (2003) IEEE Trans. Geosci. Remote Sens , vol.41 , pp. 1136-1142
    • Mayer, R.1    Bucholtz, F.2    Scribner, D.3
  • 27
    • 0036546660 scopus 로고    scopus 로고
    • Slow feature analysis: unsupervised learning of invariances
    • Wiskott L., Sejnowski T.J. Slow feature analysis: unsupervised learning of invariances. Neural Comput. 2002, 14:715-770.
    • (2002) Neural Comput. , vol.14 , pp. 715-770
    • Wiskott, L.1    Sejnowski, T.J.2
  • 28
    • 27244444336 scopus 로고    scopus 로고
    • Slow feature analysis yields a rich repertoire of complex cell properties
    • Berkes P., Wiskott L. Slow feature analysis yields a rich repertoire of complex cell properties. J. Vision. 2005, 5:579-601.
    • (2005) J. Vision. , vol.5 , pp. 579-601
    • Berkes, P.1    Wiskott, L.2
  • 30
    • 34247263840 scopus 로고    scopus 로고
    • Independent slow feature analysis and nonlinear blind source separation
    • Blaschke T., Zito T., Wiskott L. Independent slow feature analysis and nonlinear blind source separation. Neural Comput. 2007, 19:994-1021.
    • (2007) Neural Comput. , vol.19 , pp. 994-1021
    • Blaschke, T.1    Zito, T.2    Wiskott, L.3
  • 31
    • 78649642124 scopus 로고    scopus 로고
    • Pattern recognition with slow feature analysis
    • Berkes P. Pattern recognition with slow feature analysis. Cogn. Sci. EPrint Arch. (CogPrint) 2005, v4104.
    • (2005) Cogn. Sci. EPrint Arch. (CogPrint) , vol.v4104
    • Berkes, P.1
  • 32
    • 0025508756 scopus 로고
    • Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution
    • Reed I.S., Yu X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Trans. Acoust. Speech Signal Process. 1990, 38:1760-1770.
    • (1990) IEEE Trans. Acoust. Speech Signal Process. , vol.38 , pp. 1760-1770
    • Reed, I.S.1    Yu, X.2
  • 34
    • 14644390912 scopus 로고    scopus 로고
    • Using AUC and accuracy in evaluating learning algorithms
    • Jin H., Ling C.X. Using AUC and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 2005, 17:299-310.
    • (2005) IEEE Trans. Knowl. Data Eng. , vol.17 , pp. 299-310
    • Jin, H.1    Ling, C.X.2
  • 35
    • 84922794248 scopus 로고    scopus 로고
    • Change detection in industrial and urban scenes using VNIR and TIR hyperspectral imagery
    • Sixth EARSeL SIG IS Workshop
    • M. Shimoni, R. Heremans, D. Borghys, C. Perneel, Change detection in industrial and urban scenes using VNIR and TIR hyperspectral imagery, in: Sixth EARSeL SIG IS Workshop, 2009.
    • (2009)
    • Shimoni, M.1    Heremans, R.2    Borghys, D.3    Perneel, C.4
  • 36
    • 84879907089 scopus 로고    scopus 로고
    • Robust technique for anomalous change detection in airborne hyperspectral imagery based on automatic and adaptive band selection
    • Acito N., Resta S., Diani M., Corsini G. Robust technique for anomalous change detection in airborne hyperspectral imagery based on automatic and adaptive band selection. Opt. Eng. 2013, 52:36202-36208.
    • (2013) Opt. Eng. , vol.52 , pp. 36202-36208
    • Acito, N.1    Resta, S.2    Diani, M.3    Corsini, G.4
  • 37
    • 60749113928 scopus 로고    scopus 로고
    • Quantitative comparison of quadratic covariance-based anomalous change detectors
    • Theiler J. Quantitative comparison of quadratic covariance-based anomalous change detectors. Appl. Opt. 2008, 47:F12-F26.
    • (2008) Appl. Opt. , vol.47 , pp. F12-F26
    • Theiler, J.1
  • 38
    • 80955148277 scopus 로고    scopus 로고
    • Hyperspectral change detection using IR-MAD and feature reduction
    • 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
    • P. Marpu, P. Gamba, J.A. Benediktsson, Hyperspectral change detection using IR-MAD and feature reduction, in: 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2011, pp. 98-101.
    • (2011) , pp. 98-101
    • Marpu, P.1    Gamba, P.2    Benediktsson, J.A.3
  • 39
    • 0032036301 scopus 로고    scopus 로고
    • Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: new approaches to change detection studies
    • Nielsen A.A., Conradsen K., Simpson J.J. Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: new approaches to change detection studies. Remote Sens. Environ. 1998, 64:1-19.
    • (1998) Remote Sens. Environ. , vol.64 , pp. 1-19
    • Nielsen, A.A.1    Conradsen, K.2    Simpson, J.J.3
  • 40
    • 33847751877 scopus 로고    scopus 로고
    • The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data
    • Nielsen A.A. The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data. IEEE Trans. Image Process 2007, 16:463-478.
    • (2007) IEEE Trans. Image Process , vol.16 , pp. 463-478
    • Nielsen, A.A.1
  • 41
    • 49349108623 scopus 로고
    • A threshold selection method from gray-level histograms
    • Otsu N. A threshold selection method from gray-level histograms. Automatica 1975, 11:23-27.
    • (1975) Automatica , vol.11 , pp. 23-27
    • Otsu, N.1
  • 42
    • 70350347100 scopus 로고    scopus 로고
    • Unsupervised change detection in satellite images using principal component analysis and k-means clustering
    • Celik T. Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geosci. Remote Sens. Lett. 2009, 6:772-776.
    • (2009) IEEE Geosci. Remote Sens. Lett. , vol.6 , pp. 772-776
    • Celik, T.1
  • 43
    • 32244443266 scopus 로고    scopus 로고
    • Image misregistration error in change measurements
    • Wang H., Ellis E.C. Image misregistration error in change measurements. Photogramm. Eng. Remote Sens. 2005, 71:1037.
    • (2005) Photogramm. Eng. Remote Sens. , vol.71 , pp. 1037
    • Wang, H.1    Ellis, E.C.2
  • 44
    • 84877924403 scopus 로고    scopus 로고
    • Residual misregistration noise estimation in hyperspectral anomalous change detection
    • Acito N., Resta S., Diani M., Corsini G. Residual misregistration noise estimation in hyperspectral anomalous change detection. Opt. Eng. 2012, 51:111705-1-111705-10.
    • (2012) Opt. Eng. , vol.51
    • Acito, N.1    Resta, S.2    Diani, M.3    Corsini, G.4
  • 45
    • 72049130304 scopus 로고    scopus 로고
    • Improved Change Detection with Local Co-registration Adjustments
    • First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
    • B. Wohlberg, J. Theiler, Improved Change Detection with Local Co-registration Adjustments, First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2009, 1-4.
    • (2009) , pp. 1-4
    • Wohlberg, B.1    Theiler, J.2
  • 46
    • 77952050157 scopus 로고    scopus 로고
    • Symmetrized local co-registration optimization for anomalous change detection
    • Proc. SPIE Computational Imaging VIII, (SPIE)
    • B. Wohlberg, J. Theiler, Symmetrized local co-registration optimization for anomalous change detection, in: Proc. SPIE Computational Imaging VIII, (SPIE), 2010, pp. 753307.
    • (2010) , pp. 753307
    • Wohlberg, B.1    Theiler, J.2


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