Noise removal from hyperspectral images by multidimensional filtering

IEEE Transactions on Geoscience and Remote Sensing

Volumn 46, Issue 7, 2008, Pages 2061-2069

  Letexier, Damien ^a   Bourennane, Salah ^a  

^a AIX MARSEILLE UNIVERSITÉ   (France)

Author keywords

Flattening directions; Hyperspectral; Multidimensional Wiener filtering (MWF); Quadtree; Tensor

Indexed keywords

ADAPTIVE FILTERING; ALGEBRA; BOOLEAN FUNCTIONS; ESTIMATION; FILTRATION; IMAGE ENHANCEMENT; LEAD; METAL RECOVERY; SIGNAL ANALYSIS; SIGNAL TO NOISE RATIO;

COMPARATIVE STUDIES; DE-NOISING; HYPERSPECTRAL IMAGE (HSI); IN ORDER; LINE DETECTION; MULTI-DIMENSIONAL DATA; MULTIDIMENSIONAL (MD); MULTILINEAR ALGEBRA; NEW APPROACHES; NOISE REMOVAL; NOISE SUBSPACES; QUADTREE PARTITIONING; SIGNAL SUB-SPACE; SIGNAL TO NOISE RATIO (SNR); SPATIAL RESOLUTION (SR); THIRD-ORDER; VECTORIZATION; WAVELET THRESHOLDING; WIENER FILTERING;

TENSORS;

EID: 45849101292     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2008.916641     Document Type: Article

References (53)

1. S. Chang and M. Vetterli, "Spatial adaptive wavelet thresholding for image denoising," in Proc. IEEE-ICIP, 1997, pp. 374-377.
2. G. Chang, B. Yu, and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression," IEEE Trans. Image Process., vol. 9, no. 9, pp. 1532-1546, Sep. 2000.
3. R. Coifman and D. Donoho, Translation-Invariant De-Noising, vol. 103, Berlin, Germany: Springer-Verlag, 1995.
4. K. O. Egiazarian, J. T. Astola, M. Helsingius, and P. Kuosmanen, "Adaptive denoising and lossy compression of images in transform domain," J. Electron. Imaging, vol. 8, no. 3, pp. 233-245, Jul. 1999.
5. K. Romberg, H. Choi, and R. Baraniuk. "Bayesian tree-structured image modeling using wavelet domain hidden Markov models." IEEE Trans. Image Process., vol. 10, no. 7, pp. 1056-1068, Jul. 2001.
6. A. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy, "A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising," IEEE Trans. Image Process., vol. 11, no. 5, pp. 545-557. May 2002.
7. A. Plaza and C. Chang. "Impact of initialization on design of endmember extraction algorithms," IEEE Trans. Geosci. Remote Sens., vol. 44. no. 11, pp. 3397-3407, Nov. 2006.
8. B. Penna, T. Tillo, E. Magii, and G. Olmo, "Transform coding techniques for lossy hyperspectral data compression," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 5, pp. 1408-1421, May 2007.
9. L. J. Rickard, R. W. Basedow, E. F. Zalewski, P. R. Silverglate, and M. Landers, "HYDICE: An airborne system for hyperspectral imaging," in Proc. SPIE-Imaging Spectrometry of the Terrestrial Environment, G. Vane, Ed, 1993, vol. 1937, pp. 173-179.
10. L. Parra, C Spence. P. Sajda, A. Ziehe, and K. Müller, "Unmixing hyperspectral data," Adv. Neural Inf. Process. Syst., vol. 12, pp. 942-948. 2000.
11. C Chang and Q. Du, "Estimation of number of spectrally distinct signal sources in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, pp. 608-619, Mar. 2004.
12. I. Atkinson, F. Kamalabadi. and D. Jones, "Wavelet-based hyperspectral image estimation," in Proc. IGARSS, 2003, vol. 2, pp. 743-745.
13. A. Green, M. Berman, and M. Craig, "A transformation for ordering multispectral data in terms of image quality with implications for noise removal," IEEE Trans. Geosci. Remote Sens., vol. 26, no. 1. pp. 65-74, Jan. 1988.
14. 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.
15. D. Manolakis, C. Siracusa, and G. Shaw, "Hyperspectral subpixel target detection using the linear mixing model: Analysis of hyperspectral image data," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 7, pp. 1392-1409, Jul. 2001.
16. H. Kwon and N. Nasrabadi, "Kernel adaptive subspace detector for hyperspectral imagery," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 2, pp. 271-275, Apr. 2006.
17. J. A. Benediktsson, J. Palmason. and J. Sveinsson, "Classification of hyperspectral data from urban areas based on extended morphological profiles," IEEE Trans. Geosci. Remote Sens., vol. 43. no. 3. pp. 480-491, Mar. 2005.
18. F. Schmidt, S. Doute, and B. Schmitt, "WAVANGLET: An efficient supervised classifier for hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 5. pp. 1374-1385, May 2007.
19. Q. Zhang, H. Wang, R. Plemmons, and V. P. Pauca. "Spectral unmixing using nonnegative tensor factorization," in Proc. ACM-SE 45, 2007, pp. 531-532.
20. D. Donoho, "De-noising by soft-thresholding," IEEE Trans. Inf. Tlieory, vol. 41, no. 3, pp. 613-627, May 1995.
21. Y. Chang. B. Shyu, C. Cho, and J. Wang, "Adaptive post-processing for region-based fractal image compression." in Proc. DCC, Snowbird, UT, 2000, p. 549.
22. P. Dragotti, G. Poggi, and A. Ragozini. "Compression of multispectral images by three-dimensional SPIHT algorithm," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 1, pp. 416-4-28, Jan. 2000.
23. X. Tang and W. Pearlman, Three-Dimensional Wavelet Based Compression of Hyperspectral Images. New York: Springer-Verlag. 2006.
24. D. Muti and S. Bourennane, "Survey on tensor signal algebraic filtering," Signal Process., vol. 87, no. 2, pp. 237-249, Feb. 2007.
25. A. Shashua and A. Levin, "Linear images coding for regression and classification using the tensor-rank principle." in Proc. IEEE-CVPR, 2001. vol. 1, p. 42.
26. P. Kroonenberg, Three-Mode Principal Component Analysis. Leiden, The Netherlands: DSWO Press, 1983.
27. H. Kiers. "Towards a standardized notation and terminology in multiway analysis." J. Chemom., vol. 14, no. 3. pp. 105-122, May/Jun. 2000.
28. M. Alex, O. Vasilescu, and D. Terzopoulos, "Multilinear analysis of image ensembles: Tensorfaces," in Proc. ECCV, 2002, pp. 447-460.
29. P. Comon, "Tensor diagonalization, a useful tool in signal processing," in Proc. IFAC SYSID, 1994, pp. 77-82.
30. P. Comon. "Tensor decompositions, state of the art and application," in Proc. IMA Conf. Math. Signal Process., Warwick, U.K., 2000.
31. L. D. Lathauwer, B. D. Moor, and J. Vandewalle, "A multilinear singular value decomposition," SIAM J. Matrix Anal Appl., vol. 21, no. 4, pp. 1253-1278, Mar. 2000.
32. J. Nagy and M. Kilmer, "Kronecker product approximation for preconditioning in three-dimensional imaging applications," IEEE Trans. Image Process., vol. 15, no. 3. pp. 604-613, Mar. 2006.
33. C Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York: Plenum, 2003.
34. L. D. Lathauwer, "Signal processing based on multilinear algebra," Ph.D. dissertation. Electr. Eng. Dept., ESAT, Leuven, Belgium. Sep. 1997.
35. n) reduction in multilinear algebra," Lin. Alg. Appl., vol. 391. pp. 31-55, 2004.
36. H. Aghajan and T. Kailath, "Sensor array processing techniques for super resolution multi-line-fitting and straight edge detection," IEEE Trans. Image Process., vol. 2, no. 4, pp. 454-465. Oct. 1993.
37. A. Weeks, Fundamentals of Electronic Image Processing. Piscataway, NJ: IEEE Press. 1996.
38. J. Weickert, B. Romeny, and M. Viergever. "Efficient and reliable schemes for nonlinear diffusion filtering," IEEE Trans. Image Process., vol. 7, no. 3, pp. 398-410, Mar. 1998.
39. B. W. Bader and T. G. Kolda, "Algorithm 862: MATLAB tensor classes for fast algorithm prototyping." ACM Trans. Math. Softw., vol. 32, no. 4, pp. 635-653. Dec. 2006.
40. D. Muti and S. Bourennane. "Multidimensional filtering based on a tensor approach," Signal Process., vol. 85, no. 12, pp. 2338-2353, Dec. 2005.
41. D. Muti and S. Bourennane, "Multidimensional signal processing using lower rank tensor approximation," in Proc. IEEE-ICASSP, Hong Kong, pp. III-457-III-460.
42. K. Abed-Meraim, H. Maître, and P. Duhamel. "Blind multichannel image restoration using subspace based method," in Proc. IEEE-ICASSP, Hong Kong. 2003, pp. V-9-V-12.
43. J. Mendel, "Tutorial on higher order statistics (spectra) in signal processing and system theory: Theoretical results and some applications," Proc. IEEE, vol. 79, no. 3, pp. 278-305, Mar. 1991.
44. N. Yuen and B. Friedlander, "Asymptotic performance analysis of blind signal copy using fourth-order cumulants," Int. J. Adapt. Control Signal Process., vol. 48, pp. 239-265, 1996.
45. N. Yuen and B. Friedlander, "DOA estimation in multipath: An approach using fourth order cumulant," IEEE Trans. Signal Process., vol. 45, no. 5, pp. 1253-1263, May 1997.
46. H. Akaike, "A new look at the statistical model identification. " IEEE Trans. Autom. Control vol. AC-19, no. 6, pp. 716-723, Dec. 1974.
47. M. Wax and T. Kailath, "Detection of signals by information theoretic criteria," IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-33, no. 2, pp. 387-392, Apr. 1985.
48. J. Sheinvald and N. Kiriati, "On the magic of SLIDE," Mach. Vis. Appl., vol. 9, no. 5/6, pp. 251-261, Mar. 1997.
49. S. Bourennane and J. Marot, "Contour estimation by array processing methods," EURASIP J. Appl. Signal Process., vol. 2006, no. 9, pp. H-1-H-15, 2006. Article ID 95 634. DOI:10.1155/ASP/2006/95634.
50. H. Aghajan and T. Kailath, "SLIDE: Subspace-based line detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 11. pp. 1057-1073, Nov. 1994.
51. R. Roy and T. Kailath, "Esprit-estimation of signal parameters via rotational invariance techniques," IEEE Trans. Acoust., Speech, Signal Process., vol. 37, no. 7, pp. 984-995, Jul. 1989.
52. H. Samet, "The quadtree and related hierarchical data structure," ACM Comput. Surv., vol. 16, no. 2, pp. 188-260, Jun. 1984.
53. N) approximation of higher-order tensors," SIAM J. Matrix Anal. Appl., vol. 21, no. 4, pp. 1324-1342, 2000.