메뉴 건너뛰기




Volumn 7, Issue 9, 2014, Pages 3742-3754

Denoising of hyperspectral images employing two-phase matrix decomposition

Author keywords

Hyperspectral image (HSI) denoising; low rank; matrix decomposition; structured sparsity; total variation (TV)

Indexed keywords

DE-NOISING; LOW-RANK; MATRIX DECOMPOSITION; STRUCTURED SPARSITIES; TOTAL VARIATION;

EID: 84910041096     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2014.2360409     Document Type: Article
Times cited : (30)

References (42)
  • 1
    • 31344444452 scopus 로고    scopus 로고
    • Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage
    • Feb.
    • H. Othman and S. E. Qian, "Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 2, pp. 397-408, Feb. 2006.
    • (2006) IEEE Trans. Geosci. Remote Sens. , vol.44 , Issue.2 , pp. 397-408
    • Othman, H.1    Qian, S.E.2
  • 2
    • 79953194762 scopus 로고    scopus 로고
    • Subspace-based striping noise reduction in hyperspectral images
    • Apr.
    • N. Acito,M. Diani, and G. Corsini, "Subspace-based striping noise reduction in hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 4, pp. 1325-1342, Apr. 2011.
    • (2011) IEEE Trans. Geosci. Remote Sens. , vol.49 , Issue.4 , pp. 1325-1342
    • Acitom. Diani, N.1    Corsini, G.2
  • 3
    • 84875739450 scopus 로고    scopus 로고
    • Multiple-spectral-band CRFs for denoising junk bands of hyperspectral imagery
    • Apr.
    • P. Zhong and R. S. Wang, "Multiple-spectral-band CRFs for denoising junk bands of hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 4, pp. 2260-2275, Apr. 2013.
    • (2013) IEEE Trans. Geosci. Remote Sens. , vol.51 , Issue.4 , pp. 2260-2275
    • Zhong, P.1    Wang, R.S.2
  • 4
    • 84877924437 scopus 로고    scopus 로고
    • A comparative study on linear regression-based noise estimation for hyperspectral imagery
    • Apr.
    • L. R. Gao, Q. Du, B. Zhang,W. Yang, and Y. F.Wu, "A comparative study on linear regression-based noise estimation for hyperspectral imagery," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 2, pp. 488-498, Apr. 2013.
    • (2013) IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol.6 , Issue.2 , pp. 488-498
    • Gao, L.R.1    Du, Q.2    Zhang, W.3    Yang, B.4    Wu, Y.F.5
  • 6
    • 79952041437 scopus 로고    scopus 로고
    • Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage
    • Mar.
    • G. Y. Chen and S. E. Qian, "Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 3, pp. 973-980, Mar. 2011.
    • (2011) IEEE Trans. Geosci. Remote Sens. , vol.49 , Issue.3 , pp. 973-980
    • Chen, G.Y.1    Qian, S.E.2
  • 7
    • 34948839352 scopus 로고    scopus 로고
    • Denoising hyperspectral imagery and recovering junk bands using wavelets and sparse approximation
    • A. C. Zelinski and V. K. Goyal, "Denoising hyperspectral imagery and recovering junk bands using wavelets and sparse approximation," in Proc. IEEE IGARSS, 2006, pp. 387-390.
    • (2006) Proc. IEEE IGARSS , pp. 387-390
    • Zelinski, A.C.1    Goyal, V.K.2
  • 9
    • 84877927306 scopus 로고    scopus 로고
    • Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation
    • Apr.
    • Y. T. Qian and M. C. Ye, "Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation," in IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 2, pp. 499-515, Apr. 2013.
    • (2013) IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol.6 , Issue.2 , pp. 499-515
    • Qian, Y.T.1    Ye, M.C.2
  • 10
    • 45849101292 scopus 로고    scopus 로고
    • Noise removal from hyperspectral images by multidimensional filtering
    • Jul.
    • D. Letexier and S. Bourennane, "Noise removal from hyperspectral images by multidimensional filtering," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 7, pp. 2061-2069, Jul. 2008.
    • (2008) IEEE Trans. Geosci. Remote Sens. , vol.46 , Issue.7 , pp. 2061-2069
    • Letexier, D.1    Bourennane, S.2
  • 11
    • 84871645534 scopus 로고    scopus 로고
    • Nonlocal transform-domain filter for volumetric data denoising and reconstruction
    • Jan.
    • M. Maggioni, V. Katkovnik, K. Egiazarian, and A. Foi, "Nonlocal transform-domain filter for volumetric data denoising and reconstruction," IEEE Trans. Image Process., vol. 22, no. 1, pp. 119-133, Jan. 2013.
    • (2013) IEEE Trans. Image Process. , vol.22 , Issue.1 , pp. 119-133
    • Maggioni, M.1    Katkovnik, V.2    Egiazarian, K.3    Foi, A.4
  • 12
    • 48849103262 scopus 로고    scopus 로고
    • Denoising and dimensionality reduction using multilinear tools for hyperspectral images
    • Apr.
    • N. Renard, S. Bourennane, and J. Blanc-Talon, "Denoising and dimensionality reduction using multilinear tools for hyperspectral images," IEEE Geosci. Remote Sens. Lett., vol. 5, no. 2, pp. 138-142, Apr. 2008.
    • (2008) IEEE Geosci. Remote Sens. Lett. , vol.5 , Issue.2 , pp. 138-142
    • Renard, N.1    Bourennane, S.2    Blanc-Talon, J.3
  • 13
    • 84867090591 scopus 로고    scopus 로고
    • Denoising of hyperspectral images using the PARAFAC model and statistical performance analysis
    • Oct.
    • X. F. Liu, S. Bourennane, and C. Fossati, "Denoising of hyperspectral images using the PARAFAC model and statistical performance analysis," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 10, pp. 3717-3724, Oct. 2012.
    • (2012) IEEE Trans. Geosci. Remote Sens. , vol.50 , Issue.10 , pp. 3717-3724
    • Liu, X.F.1    Bourennane, S.2    Fossati, C.3
  • 14
    • 84867063792 scopus 로고    scopus 로고
    • Hyperspectral image denoising employing a spectral-spatial adaptive total variation model
    • Oct.
    • Q. Q. Yuan, L. P. Zhang, and H. F. Shen, "Hyperspectral image denoising employing a spectral-spatial adaptive total variation model," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 10, pp. 3660-3677, Oct. 2012.
    • (2012) IEEE Trans. Geosci. Remote Sens. , vol.50 , Issue.10 , pp. 3660-3677
    • Yuan, Q.Q.1    Zhang, L.P.2    Shen, H.F.3
  • 15
    • 80052320461 scopus 로고    scopus 로고
    • Simultaneous denoising and intrinsic order selection in hyperspectral imaging
    • Sep.
    • M. Farzam and S. Beheshti, "Simultaneous denoising and intrinsic order selection in hyperspectral imaging," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 9, pp. 3423-3436, Sep. 2011.
    • (2011) IEEE Trans. Geosci. Remote Sens. , vol.49 , Issue.9 , pp. 3423-3436
    • Farzam, M.1    Beheshti, S.2
  • 17
    • 0033875830 scopus 로고    scopus 로고
    • Destriping multisensor imagery with moment matching
    • Aug.
    • F. L. Gadallah, F. Csillag, and E. J. M. Smith, "Destriping multisensor imagery with moment matching," Int. J. Remote Sens., vol. 21, no. 12, pp. 2505-2511, Aug. 2000.
    • (2000) Int. J. Remote Sens. , vol.21 , Issue.12 , pp. 2505-2511
    • Gadallah, F.L.1    Csillag, F.2    Smith, E.J.M.3
  • 19
    • 79957488968 scopus 로고    scopus 로고
    • Local signal-dependent noise variance estimation from hyperspectral textural images
    • Jun.
    • M. L. Uss, B. Vozel, V. V. Lukin, and K. Chehdi, "Local signal-dependent noise variance estimation from hyperspectral textural images," IEEE J. Sel. Topics Signal Process., vol. 5, no. 3, pp. 469-486, Jun. 2011.
    • (2011) IEEE J. Sel. Topics Signal Process. , vol.5 , Issue.3 , pp. 469-486
    • Uss, M.L.1    Vozel, B.2    Lukin, V.V.3    Chehdi, K.4
  • 20
    • 0019390131 scopus 로고
    • Radiometric equalization of nonperiodic striping in satellite data
    • Jul.
    • V. R. Algazi and G. E. Ford, "Radiometric equalization of nonperiodic striping in satellite data," Comput. Graph. Image Process., vol. 16, no. 3, pp. 287-295, Jul. 1981.
    • (1981) Comput. Graph. Image Process. , vol.16 , Issue.3 , pp. 287-295
    • Algazi, V.R.1    Ford, G.E.2
  • 21
    • 85032751896 scopus 로고    scopus 로고
    • Hyperspectral image data analysis
    • Jan.
    • D. Landgrebe, "Hyperspectral image data analysis," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 17-28, Jan. 2002.
    • (2002) IEEE Signal Process. Mag. , vol.19 , Issue.1 , pp. 17-28
    • Landgrebe, D.1
  • 23
    • 84861772901 scopus 로고    scopus 로고
    • Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches
    • Apr.
    • J. M. Bioucas-Dias et al., "Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 354-379, Apr. 2012.
    • (2012) IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol.5 , Issue.2 , pp. 354-379
    • Bioucas-Dias, J.M.1
  • 24
    • 79952493979 scopus 로고    scopus 로고
    • Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization
    • J. Wright, A. Ganesh, S. Rao, Y. G. Peng, and Y. Ma, "Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization," in Proc. Neural Inform. Process. Syst., 2009, pp. 1-9.
    • (2009) Proc. Neural Inform. Process. Syst. , pp. 1-9
    • Wright, J.1    Ganesh, A.2    Rao, S.3    Peng, Y.G.4    Ma, Y.5
  • 25
    • 79960675858 scopus 로고    scopus 로고
    • Robust principal component analysis?
    • May
    • E. J. Candés, X. D. Li, Y.Ma, and J. Wright, "Robust principal component analysis?" J. ACM, vol. 58, no. 3, pp. 11:1-11:37, May 2011.
    • (2011) J. ACM , vol.58 , Issue.3 , pp. 111-1137
    • Candés, E.J.1    Li, X.D.2    Ma, Y.3    Wright, J.4
  • 26
    • 73049115925 scopus 로고    scopus 로고
    • Sparsity and persistence: Mixed norms provide simple signal models with dependent coefficients
    • Sep.
    • M. Kowalski and B. Torrésani, "Sparsity and persistence: mixed norms provide simple signal models with dependent coefficients," Signal Image, Video Process., vol. 3, no. 3, pp. 251-264, Sep. 2009.
    • (2009) Signal Image, Video Process. , vol.3 , Issue.3 , pp. 251-264
    • Kowalski, M.1    Torrésani, B.2
  • 27
    • 70049113231 scopus 로고    scopus 로고
    • Sparse regression using mixed norms
    • Nov.
    • M. Kowalski, "Sparse regression using mixed norms," Appl. Comput. Harmon. Anal., vol. 27, no. 3, pp. 303-324, Nov. 2009.
    • (2009) Appl. Comput. Harmon. Anal. , vol.27 , Issue.3 , pp. 303-324
    • Kowalski, M.1
  • 28
    • 34548228266 scopus 로고    scopus 로고
    • An iterative algorithm for nonlinear inverse problems with joint sparsity constraints in vector-valued regimes and an application to color image inpainting
    • G. Teschke and R. Ramlau, "An iterative algorithm for nonlinear inverse problems with joint sparsity constraints in vector-valued regimes and an application to color image inpainting," Inverse Probl., vol. 23, no. 5, pp. 1851-1870, 2007.
    • (2007) Inverse Probl. , vol.23 , Issue.5 , pp. 1851-1870
    • Teschke, G.1    Ramlau, R.2
  • 29
    • 36749019495 scopus 로고    scopus 로고
    • Recovery algorithm for vector-valued data with joint sparsity constraints
    • M. Fornasier and H. Rauhut, "Recovery algorithm for vector-valued data with joint sparsity constraints," SIAM J. Numer. Anal., vol. 46, no. 2, pp. 577-613, 2008.
    • (2008) SIAM J. Numer. Anal. , vol.46 , Issue.2 , pp. 577-613
    • Fornasier, M.1    Rauhut, H.2
  • 31
    • 44049111982 scopus 로고
    • Nonlinear total variation based noise removal algorithms
    • Nov.
    • L. I. Rudin, S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," Phys. D. Nonlinear Phenom., vol. 60, no. 1-4, pp. 259-268, Nov. 1992.
    • (1992) Phys. D. Nonlinear Phenom. , vol.60 , Issue.1-4 , pp. 259-268
    • Rudin, L.I.1    Osher, S.2    Fatemi, E.3
  • 32
    • 0347900511 scopus 로고    scopus 로고
    • Modeling textures with total variation minimization and oscillating patterns in image processing
    • Dec.
    • L. A. Vese and S. Osher, "Modeling textures with total variation minimization and oscillating patterns in image processing," J. Sci. Comput., vol. 19, no. 1-3, pp. 553-572, Dec. 2003.
    • (2003) J. Sci. Comput. , vol.19 , Issue.1-3 , pp. 553-572
    • Vese, L.A.1    Osher, S.2
  • 35
    • 84881306037 scopus 로고    scopus 로고
    • Sparse and low-rank matrix decomposition via alternating direction method
    • X. M. Yuan and J. F. Yang, "Sparse and low-rank matrix decomposition via alternating direction method," Pac. J. Optim., vol. 9, no. 1, pp. 167-180, 2013.
    • (2013) Pac. J. Optim. , vol.9 , Issue.1 , pp. 167-180
    • Yuan, X.M.1    Yang, J.F.2
  • 39
    • 1942436689 scopus 로고    scopus 로고
    • Image quality assessment: From error visibility to structural similarity
    • Apr.
    • Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Trans. Image Process., vol. 13, no. 4, p. 600C612, Apr. 2004.
    • (2004) IEEE Trans. Image Process. , vol.13 , Issue.4 , pp. 600C612
    • Wang, Z.1    Bovik, A.C.2    Sheikh, H.R.3    Simoncelli, E.P.4
  • 41
    • 84910048137 scopus 로고    scopus 로고
    • [Online] , accessed on Oct. 14, 2013
    • [Online]. Available: http://www.tec.army.mil/hypercube, accessed on Oct. 14, 2013.
  • 42
    • 20444432773 scopus 로고    scopus 로고
    • Kernel-based methods for hyperspectral image classification
    • Jun.
    • G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1351-1362, Jun. 2005
    • (2005) IEEE Trans. Geosci. Remote Sens. , vol.43 , Issue.6 , pp. 1351-1362
    • Camps-Valls, G.1    Bruzzone, L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.