Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 6, Issue 2, 2013, Pages 499-515

  Qian, Yuntao ^a   Ye, Minchao ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Hyperspectral imagery; noise reduction; nonlocal similarity; sparse representation; variance stabilizing transformation

Indexed keywords

HYPER-SPECTRAL IMAGERIES; HYPERSPECTRAL REMOTE SENSING DATA; NOISE REDUCTION METHODS; NON-LOCAL SIMILARITIES; REDUNDANT DICTIONARIES; SPARSE REPRESENTATION; STRUCTURED SPARSE REPRESENTATIONS; VARIANCE-STABILIZING TRANSFORMATION;

GAUSSIAN NOISE (ELECTRONIC); IMAGE PROCESSING; NOISE ABATEMENT; SPACE APPLICATIONS; SPECTROSCOPY; SPEECH ENHANCEMENT;

REMOTE SENSING;

DATA SET; GAUSSIAN METHOD; IMAGE PROCESSING; IMAGERY; MODEL VALIDATION; POISSON RATIO; REMOTE SENSING; SPECTRAL ANALYSIS;

EID: 84877927306     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2012.2232904     Document Type: Article

References (54)

1. R. Phillips, C. Blinn, L. Watson, and R. Wynne, "An adaptive noisefiltering algorithm for AVIRIS data with implications for classification accuracy," IEEE Trans. Geosci. Remote Sens. , vol. 47, no. 9, pp. 3168-3179, 2009.
2. J. Martin-Herrero, "Anisotropic diffusion in the hypercube," IEEE Trans. Geosci. Remote Sens. , vol. 45, no. 5, pp. 1386-1398, 2007. (Pubitemid 46652339)
3. 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, 2008.
4. G. Chen and S. Qian, "Denoising and dimensionality reduction of hyperspectral imagery using wavelet packets, neighbour shrinking and principal component analysis," Int. J. Remote Sens. , vol. 30, no. 18, pp. 4889-4895, 2009.
5. J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. Huang, and S. Yan, "Sparse representation for computer vision and pattern recognition," Proc. IEEE, vol. 98, no. 6, pp. 1031-1044, 2010.
6. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Berlin, Germany: Springer Verlag, 2010.
7. M. Jafari and M. Plumbley, "Fast dictionary learning for sparse representations of speech signals," IEEE J. Select. Topics Signal Process. , vol. 5, no. 5, pp. 1025-1031, 2011.
8. M. Elad, M. Figueiredo, and Y. Ma, "On the role of sparse and redundant representations in image processing," Proc. IEEE, vol. 98, no. 6, pp. 972-980, 2010.
9. M. Iordache, J. M. Bioucas-Dias, and A. Plaza, "Sparse unmixing of hyperspectral data," IEEE Trans. Geosci. Remote Sens. , vol. 49, no. 6, pp. 2014-2039, 2011.
10. A. Castrodad, Z. Xing, J. Greer, E. Bosch, L. Crrin, and G. Sapiro, "Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery," IEEE Trans. Geosci. Remote Sens. , vol. 49, no. 11, pp. 4263-4281, 2011.
11. Y. Chen, N. Nasrabadi, and T. Tran, "Sparse representation for target detection in hyperspectral imagery," IEEE J. Select. Topics Signal Process. , vol. 5, no. 3, pp. 629-640, 2011.
12. Y. Qian, M. Ye, and Q. Wang, "Noise reduction of hyperspectral imagery using nonlocal sparse representation with spectral-spatial structure," in Proc. IEEE Int. Geosci. Remote Sens. Symp. , IGARSS, 2012, pp. 3467-3470.
13. M. Ye and Y. Qian, "Mixed Poisson-Gaussian noise model based sparse denoising for hyperspectral imagery," in Proc. 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2012.
14. S. Bourguignon, D. Mary, and E. Slezak, "Sparsity-based denoising of hyperspectral astrophysical data with colored noise: Application to the MUSE instrument," in Proc. 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010.
15. A. Karami, M. Yazdi, and A. Zolghadre Asli, "Noise reduction of hyperspectral images using kernel non-negative Tucker decomposition," IEEE J. Select. Topics Signal Process. , vol. 5, no. 3, pp. 487-493, 2011.
16. B. Rasti, J. R. Sveinsson, M. O. Ulfarsson, and J. A. Benediktsson, "Hyperspectral image denoising using 3D wavelets," in Proc. IEEE Int. Geosci. Remote Sens. Symp. , IGARSS, 2012, pp. 1349-1352.
17. E. Christophe, C. Mailhes, and P. Duhamel, "Hyperspectral image compression: adapting SPIHT and EZW to anisotropic 3-D wavelet coding," IEEE Trans. Image Process. , vol. 17, no. 12, pp. 2334-2346, 2010.
18. Y. Qian,M. Ye, and J. Zhou, "Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features," IEEE Trans. Geosci. Remote Sens. , pp. 1-16, 2012.
19. L. Shen and S. Jia, "Three-dimensional Gabor wavelets for pixel-based hyperspectral imagery classification," IEEE Trans. Geosci. Remote Sens. , vol. 49, no. 12, pp. 5039-5046, 2011.
20. A. Buades, B. Coll, and J. Morel, "A review of image denoising algorithms, with a new one," SIAM J. Multiscale Model. Sim. , vol. 4, no. 2, pp. 490-530, 2005. (Pubitemid 43890582)
21. M. Mahmoudi and G. Sapiro, "Fast image and video denoising via nonlocal means of similar neighborhoods," IEEE Signal Process. Lett. , vol. 12, no. 12, pp. 839-842, 2005. (Pubitemid 41800447)
22. Y. Qian, Y. Shen, M. Ye, and Q. Wang, "3-D nonlocal means filter with noise estimation for hyperspectral imagery denoising," in Proc. IEEE Int. Geosci. Remote Sens. Symp. , IGARSS, 2012, pp. 1345-1348.
23. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, "Non-local sparse models for image restoration," in Proc. 12th IEEE Int. Conf. Comp. Vis. , 2009, pp. 2272-2279.
24. W. Dong,X. Li, L. Zhang, andG. Shi, "Sparsity-based image denoising via dictionary learning and structural clustering," in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2011, pp. 457-464.
25. B. Ophir, M. Lustig, and M. Elad, "Multi-scale dictionary learning using wavelets," IEEE J. Select. Topics Signal Process. , vol. 5, no. 5, pp. 1014-1024, 2011.
26. Z. Xing, M. Zhou, A. Castrodad, G. Sapiro, and L. Carin, "Dictionary learning for noisy and incomplete hyperspectral images," SIAM J. Imag. Sci. , vol. 5, no. 1, pp. 33-56, 2012.
27. J. Richards and X. Jia, Remote Sensing Digital Image Analysis, 4th ed. Berlin, Germany: Springer Verlag, 2006.
28. M. Uss, B. Vozel, V. Lukin, and K. Chehdi, "Local signal-dependent noise variance estimation from hyperspectral textural images," IEEE J. Select. Topics Signal Process. , vol. 5, no. 3, pp. 469-486, 2011.
29. N. Acito, M. Diani, and G. Corsini, "Signal-dependent noise modeling and model parameter estimation in hyperspectral images," IEEE Trans. Geosci. Remote Sens. , vol. 49, no. 8, pp. 2957-2971, 2011.
30. B. Aiazzi, L. Alparone, A. Barducci, S. Baronti, P. Marcoionni, I. Pippi, and M. Selva, "Noise modelling and estimation of hyperspectral data from airborne imaging spectrometers," Ann. Geophys. , vol. 49, no. 1, pp. 1-9, 2006. (Pubitemid 44226622)
31. A. Foi, M. Trimeche, V. Katkovnik, and K. Egiazarian, "Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data," IEEE Trans. Image Process. , vol. 17, no. 10, pp. 1737-1754, 2008.
32. F. Anscombe, "The transformation of Poisson, binomial and negativebinomial data," Biometrika, vol. 35, no. 3-4, pp. 246-254, 1948.
33. F. Murtagh, J. Starck, and A. Bijaoui, "Image restoration with noise suppression using a multiresolution support," Astronomy and Astrophysics, Suppl. Ser. , vol. 112, pp. 179-189, 1995.
34. A. Börner, L. Wiest, P. Keller, R. Reulke, R. Richter, M. Schaepman, and D. Schläpfer, "SENSOR: A tool for the simulation of hyperspectral remote sensing systems," ISPRS J. Photogramm. Remote Sens. , vol. 55, no. 5-6, pp. 299-312, 2001. (Pubitemid 32368403)
35. H. Jones, D. Haaland, M. Sinclair, D. Melgaard, M. Van Benthem, and M. Pedroso, "Weighting hyperspectral image data for improved multivariate curve resolution results," J. Chemometr. , vol. 22, no. 9, pp. 482-490, 2008.
36. L. Gao, Q. Du,W. Yang, and B. Zhang, "A comparative study on noise estimation for hyperspectral imagery," in Proc. 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2012.
37. R. Wrigley, D. Card, C. Hlavka, J. Hall, F. Mertz, C. Archwamety, and R. Schowengerdt, "Thematic mapper image quality: Registration, noise, and resolution," IEEE Trans. Geosci. Remote Sens. , vol. 22, no. 3, pp. 263-271, 1984. (Pubitemid 14608493)
38. P. Curran and J. Dungan, "Estimation of signal-to-noise: A new procedure applied to AVIRIS data," IEEE Trans. Geosci. Remote Sens. , vol. 27, no. 5, pp. 620-628, 1989. (Pubitemid 20618512)
39. B. Gao, "An operational method for estimating signal to noise ratios from data acquired with imaging spectrometers," Remote Sens. Environ. , vol. 43, no. 1, pp. 23-33, 1993. (Pubitemid 23634038)
40. R. Roger and J. Arnold, "Reliably estimating the noise in AVIRIS hyperspectral images," Int. J. Remote Sens. , vol. 17, no. 10, pp. 1951-1962, 1996.
41. J. Mairal, M. Elad, and G. Sapiro, "Sparse representation for color image restoration," IEEE Trans. Image Process. , vol. 17, no. 1, pp. 53-69, 2008.
42. J. Starck, M. Elad, and D. Donoho, "Image decomposition via the combination of sparse representations and a variational approach," IEEE Trans. Image Process. , vol. 14, no. 10, pp. 1570-1582, 2005. (Pubitemid 41623087)
43. R. Tibshirani, "Regression shrinkage and selection via the lasso: a retrospective," J. R. Statist. Soc. B, vol. 73, pp. 273-282, 2011.
44. A. Beck and M. Teboulle, "A fast iterative shrinkage-thresholding algorithm for linear inverse problems," SIAM J. Imag. Sci. , vol. 2, no. 1, pp. 183-202, 2009.
45. J. Liu, S. Ji, and J. Ye, "SLEP: Sparse Learning With Efficient Projections," Arizona State Univ. , 2009 [Online] . Available: http://www. public. asu. edu/jye02/Software/SLEP/
46. X. Yuan and S. Yan, "Visual classification with multi-task joint sparse representation," in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2010, pp. 3493-3500.
47. J. Liu, S. Ji, and J. Ye, "Multi-task feature learning via efficient , 1-norm minimization," in Proc. 25th Conf. Uncertainty in Artificial Intelligence, 2009, pp. 339-348.
48. X. Chen, W. Pan, J. Kwok, and J. Carbonell, "Accelerated gradient method for multi-task sparse learning problem," in Proc. 9th IEEE Int. Conf. Data Mining, 2009, pp. 746-751.
49. G. Camps-Valls, L. Gomez-Chov, J. Muñoz-Marí, J. Vila-Francés, and J. Calpe-Maravilla, "Composite kernels for hyperspectral image classification," IEEE Geosci. Remote Sens. Lett. , vol. 3, no. 1, pp. 93-97, 2006. (Pubitemid 43131904)
50. R. Clark, G. Swayze, R. Wise, E. Livo, T. Hoefen, R. Kokaly, and S. Sutley, USGS Digital Spectral Library Splib06a, 2007 [Online] . Available: http://speclab. cr. usgs. gov/spectral-lib. html
51. D. Donoho and J. Johnstone, "Ideal spatial adaptation by wavelet shrinkage," Biometrika, vol. 81, no. 3, pp. 425-455, 1994.
52. M. Banham and A. Katsaggelos, "Digital image restoration," IEEE Signal Process. Mag. , vol. 14, no. 2, pp. 24-41, 1997. (Pubitemid 127571670)
53. J. Li, J. Bioucas-Dias, and A. Plaza, "Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields," IEEE Trans. Geosci. Remote Sens. , vol. 50, no. 3, pp. 809-823, 2012.
54. D. Donoho, "De-noising by soft-thresholding," IEEE Trans. Inf. Theory, vol. 41, no. 3, pp. 613-627, 1995.