Hyperspectral imagery denoising based on oblique subspace projection

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

Volumn 7, Issue 6, 2014, Pages 2468-2480

  Wang, Qian ^a,b   Zhang, Lifu ^a   Tong, Qingxi ^a   Zhang, Feizhou ^c  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c PEKING UNIVERSITY   (China)

Author keywords

Denoising; hyperspectral image; oblique subspace projection (OBSP); orthogonal subspace projection (OSP)

Indexed keywords

ALGORITHMS; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SPECTROSCOPY; VECTORS;

DE-NOISING; DE-NOISING ALGORITHM; GAUSSIAN FILTERING; HYPER-SPECTRAL IMAGERIES; HYPER-SPECTRAL IMAGES; OBLIQUE SUBSPACES; ORTHOGONAL SUBSPACE PROJECTION; POLYNOMIAL MODELING;

IMAGE TEXTURE;

ALGORITHM; CORRELATION; GAUSSIAN METHOD; HYPERION; PRINCIPAL COMPONENT ANALYSIS; SATELLITE IMAGERY; SIGNAL-TO-NOISE RATIO; SPECTRAL ANALYSIS;

HYPERION;

EID: 84905907566     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2014.2329322     Document Type: Article

References (40)

1. A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, "Imaging spectrometry for earth remote-sensing," Science, vol. 228, pp. 1147-1153, 1985.
2. A. F. H. Goetz, "Hyperspectral imaging and quantitative remote sensing," in Proc. Land Satellite Information in the Next Decade, 1995, pp. E1-E10.
3. A. F. H. Goetz, "Three decades of hyperspectral remote sensing of the earth: A personal view," Remote Sens. Environ., vol. 113, pp. S5-S16, 2009.
4. Q. Tong, Y. Xue, and L. Zhang, "Progress in hyperspectral remote sensing science and technology in China over the past three decades," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 1, pp. 70-91, Jan. 2014.
5. B. Aiazzi et al., "Noise modelling and estimation of hyperspectral data from airborne imaging spectrometers," Ann. Geophys., vol. 49, pp. 1-9, 2006. (Pubitemid 44226622)
6. L. Gao, B. Zhang, X. Zhang, and Q. Shen, "Study on the method for estimating the noise in remote sensing images based on local standard deviations," J. Remote Sens., vol. 11, pp. 201-208, 2007.
7. P. Toivanen, A. Kaarna, J. Mielikainen, and M. Laukkanen, "Noise reduction methods for hyperspectral images," in Image and Signal Processing for Remote Sensing Viii, vol. 4885, S. B. Serpico, Ed. Bellingham, WA, USA: SPIE Int. Soc. Opt. Eng., 2003, pp. 307-313.
8. Y. Qian and M. Ye, "Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 2, pp. 499-515, Apr. 2013.
9. L. Gao and Q. Du, "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.
10. P. Latorre-Carmona, J. Martinez Sotoca, F. Pla, J. Bioucas-Dias, and C. J. Ferre, "Effect of denoising in band selection for regression tasks in hyperspectral datasets," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 2, pp. 473-481, Apr. 2013.
11. J. Wang, Y. Wang, and C. Li, "Noise model of hyperspectral imaging system and influence on radiation sensitivity," J. Remote Sens., vol. 14, pp. 607-620, 2010.
12. C. I. Chang, "Orthogonal subspace projection (OSP) revisited: A comprehensive study and analysis," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 502-518, Mar. 2005.
13. C. I. Chang, W. Xiong, H. M. Chen, and J. W. Chai, "Maximum orthogonal subspace projection approach to estimating the number of spectral signal sources in hyperspectral imagery," IEEE J. Sel. Topics Signal Process., vol. 5, no. 3, pp. 504-520, Jun. 2011.
14. Q. Du, H. Ren, and C. I. Chang, "A comparative study for orthogonal subspace projection and constrained energy minimization," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 6, pp. 1525-1529, Jun. 2003.
15. J. C. Harsanyi, "Detection and classification of subpixel signatures in hyperspectral image sequences," Ph. D. dissertation, Dept. Comput. Sci. Electr. Eng., Univ. Maryland, Baltimore, MD, USA, 1993.
16. C. I. Chang and Q. Du, "An interference rejection approach to noise adjusted principal components transform," in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS)-Sens. Manag. Environ., 1998, vol. 1-5, pp. 2059-2061.
17. C. I. Chang and Q. Du, "Interference and noise-adjusted principal components analysis," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 5, pp. 2387-2396, Sep. 1999.
18. Q. Du, "Interference and noise adjusted principal components analysis for hyperspectral image compression," in Chemical and Biological Standoff Detection II, vol. 5584, J. O. Jensen and J. M. Theriault, Eds. Bellingham, WA, USA: SPIE Int. Soc. Opt. Eng., 2004, pp. 186-193. (Pubitemid 40479955)
19. Q. Du and N. Raksuntorn, "Hyperspectral image analysis using noiseadjusted principal component transform," in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII Pts 1 and 2, vol. 6233, S. S. Shen and P. E. Lewis, Eds., Bellingham, WA, USA: SPIE Int. Soc. Opt. Eng., 2006 pp. F2330-F2330.
20. Q. Du and H. Szu, "Interference and noise adjusted principal components analysis for hyperspectral remote sensing image compression," in Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, vol. 6247, H. H. Szu Ed., Bellingham,WA,USA: SPIE Int. Soc. Opt. Eng., 2006 pp. A2470-A2470.
21. R. E. Roger, "Principal components transform with simple, automatic noise adjustment," Int. J. Remote Sens., vol. 17, pp. 2719-2727, 1996.
22. B. Xu and P. Gong, "Noise estimation in a noise-adjusted principal component transformation and hyperspectral image restoration," Can. J. Remote Sens., vol. 34, pp. 271-286, Jun. 2008.
23. A. Dadon, E. Ben-Dor, and A. Karnieli, "Use of derivative calculations and minimum noise fraction transform for detecting and correcting the spectral curvature effect (smile) in hyperion images," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 6, pp. 2603-2612, Jun. 2010.
24. C. Gordon, "A generalization of the maximum noise fraction transform," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 1, pp. 608-610, Jan. 2000. (Pubitemid 30556222)
25. A. A. Nielsen, "Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Trans. Image Process., vol. 20, no. 3, pp. 612-624, Mar. 2011.
26. L. Alparone, M. Selva, B. Aiazzi, S. Baronti, F. Butera, L. Chiarantini, "Signal-dependent noise modelling and estimation of new-generation imaging spectrometers," in Proc. 1st Workshop Hyperspectral Image Signal Process.: Evol. Remote Sens. (WHISPERS'09), Grenoble, France, Aug. 2009, pp. 364-367.
27. B. Aiazzi et al., "Hyperspectral noise modelling and estimation," in Proc. Hyperspectral Workshop, 2010, pp. 17-19.
28. 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.
29. G. Corsini, M. Dian, and T. Walzer, "Striping removal in MOS-B data," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 3, pp. 1439-1446, May 2000.
30. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed., Beijing, China: Publishing House of Electronics Industry, 2009.
31. D. Zhang, Digital Image Processing Using MatLab. Beijing, China: Posts& Telecom Press, 2009.
32. J. Harsanyi, W. Farrand, and C.-I. Chang, "Determining the number and identity of spectral endmembers: An integrated approach using Neyman-Pearson eigenthresholding and iterative constrained RMS error minimization," in Proc. 9th Thematic Conf. Geologic Remote Sens., 1993, pp. 395-408. (Pubitemid 24394175)
33. H. V. Poor, An Introduction to Detection and Estimation Theory, 2nd ed. NewYork, NY, USA: Springer, 1994.
34. C. I. 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.
35. P. A. Wedin, "On angles between subspaces of a finite dimensional inner product space," in Matrix Pencils, Lecture Notes in Mathematics, vol. 973, B. Kagstrom and A. Ruhe, Eds. New York, NY, USA: Springer, 1983, pp. 263-285.
36. A. Bjorck and G. Golub, "Numerical methods for computing angles between linear subspaces," Math. Comput. vol. 27, pp. 579-594, 1973.
37. Y. Cheng, K. Zhang, and Z. Xu, Theory of Matrix, 3rd ed. Xi'an, China: Northwestern Polytechnic Univ. Press, 2006.
38. R. T. Behrens and L. L. Scharf, "Signal processing applications of oblique projection operators," IEEE Trans. Signal Process., vol. 42, no. 6, pp. 1413-1424, Jun. 1994.
39. R. T. Behrens and L. L. Scharf, "Corrections to signal processing applications of oblique projection operators," IEEE Trans. Signal Process., vol. 44, no. 5, p. 1300, May 1996.
40. 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, pp. 600-612, Apr. 2004.