A data-driven stochastic approach for unmixing hyperspectral imagery

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

Volumn 7, Issue 6, 2014, Pages 1936-1946

  Bhatt, Jignesh S ^a   Joshi, Manjunath V ^a   Raval, Mehul S ^b  

^a DHIRUBHAI AMBANI INSTITUTE OF INFORMATION AND COMMUNICATION TECHNOLOGY   (India)

^b AHMEDABAD UNIVERSITY   (India)

Author keywords

Abundance estimation; Huber function; hyperspectral imaging; Markov random field (MRF); spectral unmixing

Indexed keywords

BAYESIAN NETWORKS; MARKOV PROCESSES; PARTICLE SWARM OPTIMIZATION (PSO); PROBABILITY DISTRIBUTIONS; SIGNAL TO NOISE RATIO; STOCHASTIC SYSTEMS;

ABUNDANCE ESTIMATION; HUBER FUNCTION; HYPERSPECTRAL IMAGING; MARKOV RANDOM FIELDS; SPECTRAL UNMIXING;

SPECTROSCOPY;

ALGORITHM; AVIRIS; BAYESIAN ANALYSIS; MARKOV CHAIN; REMOTE SENSING; SATELLITE DATA; SATELLITE IMAGERY; STOCHASTICITY;

EID: 84905898474     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2014.2328597     Document Type: Article

References (49)

1. A. F. H. Goetz et al., "Imaging spectrometry for earth remote sensing," Science, vol. 228, no. 4704, pp. 1147-1153, 1985.
2. J. Boardman, "Analysis, understanding and visualization of hyperspectral data as convex sets in n-space," in Proc. SPIE, vol. 2480, pp. 23-36, 1995.
3. A. D. Stocker and A. P. Schaum, "Application of stochastic mixing models to hyperspectral detection problems," in Proc. SPIE, vol. 3071, pp. 47-60, 1997.
4. M. T. Eismann and R. C. Hardie, "Initialization and convergence of the stochastic mixing model," in Proc. SPIE, vol. 5159, pp. 307-318, 2003.
5. J. 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.
6. A. Plaza et al., "Recent advances in techniques for hyperspectral image processing," Remote Sens. Environ., vol. 113 (Suppl. 1), pp. S110-S122, 2009.
7. A. Plaza, G. Mart, J. Plaza, M. Zortea, and S. Sergio, "Recent developments in endmember extraction and spectral unmixing," Opt. Remote Sens. Adv. Sig. Process. Explo. Tech., pp. 1-33, 2010.
8. 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.
9. [Online] Available: http://speclab.cr.usgs.gov/spectral.lib06/splib06a
10. M. E. Winter, "N-findr: An algorithm for fast autonomous spectral endmember determination in hyperspectral data," in Proc. SPIE, vol. 3753, pp. 266-277, 1999.
11. J. Nascimento and J. Dias, "Vertex component analysis: A fast algorithm to unmix hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 4, pp. 898-910, Apr. 2005. (Pubitemid 40476033)
12. A. Plaza, P. Martinez, R. Perez, and J. Plaza, "Spatial/spectral endmember extraction by multidimensional morphological operations," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 9, pp. 2025-2041, Sep. 2002. (Pubitemid 35458399)
13. G. Martin and A. Plaza, "Spatial-spectral preprocessing prior to endmember identification and unmixing of remotely sensed hyperspectral data," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 380-395, Apr. 2012.
14. C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York, NY, USA: Kluwer, 2003.
15. L. Parra, K.-R. Mueller, C. Spence, A. Ziehe, and P. Sajda, "Unmixing hyperspectral data," in Proc. Adv. Neural Inf. Processes Syst. (NIPS), 2000, vol. 12, pp. 942-948.
16. N. Keshava, "A survey of spectral unmixing algorithms," Lincoln Lab. J., vol. 14, no. 1, pp. 55-78, 2003.
17. P. Masson and W. Pieczynski, "SEM algorithm and unsupervised statistical segmentation of satellite images," IEEE Trans. Geosci. Remote Sens., vol. 31, no. 3, pp. 618-633, May 1993. (Pubitemid 23697387)
18. S. Mei, M. He, Z. Wang, and D. Feng, "Mixture analysis by multichannel Hopfield neural network," IEEE Geosci. Remote Sens. Lett., vol. 7, no. 3, pp. 455-459, Jul. 2010.
19. D. Heinz and C.-I. Chang, "Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 3, pp. 529-545, Mar. 2001. (Pubitemid 32400422)
20. M. T. Eismann and R. C. Hardie, "Stochastic spectral unmixing with enhanced endmember class separation," Appl. Optics, vol. 43, no. 36, pp. 6596-6608, 2004. (Pubitemid 40120775)
21. Y. Hu, H. Lee, and F. Scarpace, "Optimal linear spectral unmixing," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 1, pp. 639-644, Jan. 1999.
22. B. Sirkeci, D. Brady, and J. Burman, "Restricted total least squares solutions for hyperspectral imagery," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP'00), 2000, vol. 1, pp. 624-627.
23. J. S. Bhatt, M. V. Joshi, and M. S. Raval, "Aregularization based method for spectral unmixing of imaging spectrometer data," in Proc. SPIE, vol. 8537, pp. 85370J1-85370J7, 2012.
24. L. Miao, H. Qi, and H. Szu, "Amaximum entropy approach to unsupervised mixed-pixel decomposition," IEEE Trans. Image Process., vol. 16, no. 4, pp. 1008-1021, Apr. 2007. (Pubitemid 46546106)
25. S. Moussaoui, C. Carteret, C. Brie, and A. Mohammad-Djafari, "Bayesian analysis of spectral mixture data using Markov chain Monte Carlo methods," Chemom. Intell. Lab. Syst., vol. 81, no. 2, pp. 137-148, 2006.
26. F. Schmidt et al., "Implementation strategies for hyperspectral unmixing using bayesian source separation," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4000-4013, Nov. 2010.
27. R. Rand and D. Keenan, "A spectral mixture process conditioned by Gibbsbased partitioning," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 7, pp. 1421-1434, Jul. 2001. (Pubitemid 32732656)
28. O. Eches, N. Dobigeon, and J.-Y. Tourneret, "Enhancing hyperspectral image unmixing with spatial correlations," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 11, pp. 4239-4247, Nov. 2011.
29. J. S. Bhatt, M. V. Joshi, and M. S. Raval, "A parametric statistical model over spectral space for the unmixing of imaging spectrometer data," in Proc. SPIE, vol. 8537, pp. 85371J1-85371J7, 2012.
30. P. J. Huber, "Robust estimation of a location parameter," Ann. Math. Statist., vol. 35, no. 1, pp. 73-101, 1964.
31. A. P. Schaum and A. D. Stocker, "Subpixel detection methods: Spectral unmixing, correlation processing and when they are appropriate," in Proc. Int. Symp. Spectral Sens. Res., 1994.
32. B. Shahraray and D. Anderson, "Optimal estimation of contour properties by cross-validated regularization," IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 6, pp. 600-610, Jun. 1989.
33. T. P. Minka, "Estimating a Dirichlet distribution," Media Lab, Cambridge, MA, MIT Tech. Rep., 2000, revised 2003, 2009.
34. J. W. Boardman and F. A. Kruse, "Analysis of imaging spectrometer data using n-dimensional geometry and a mixture-tuned matched filtering approach," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 11, pp. 4138-4152, Nov. 2011.
35. J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. IEEE Int. Conf. Neural Netw., 1995, vol. 4, pp. 1942-1948.
36. B. Zhang, X. Sun, L. Gao, and L. Yang, "Endmember extraction of hyperspectral remote sensing images based on the discrete particle swarm optimization algorithm," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 11, pp. 4173-4176, Nov. 2011.
37. H. Yang, Q. Du, and G. Chen, "Particle swarm optimization-based hyperspectral dimensionality reduction for urban land cover classification, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 544-554, Apr. 2012.
38. W.-K. Ma et al., "A signal processing perspective on hyperspectral unmixing: Insights from remote sensing," IEEE Signal Process. Mag., vol. 31, no. 1, pp. 67-81, Jan. 2014.
39. R. Bro and S. Jong, "A fast non-negativity constrained least squares algorithm," J. Chemom., vol. 11, pp. 393-401, 1997. (Pubitemid 127478570)
40. [Online]. Available: ftp://popo.jpl.nasa.gov/pub/free-data/f080611t01p00r 06rdn-c/
41. Z. Wang and A. C. Bovik, "Mean squared error: Love it or leave it? A new look at signal fidelity measures," IEEE Signal Process. Mag., vol. 26, no. 1, pp. 98-117, Jan. 2009.
42. R. Yuhans, A. Goetz, and J. Boardman, "Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm," in Proc. Summaries 3rd Ann. JPL Airborne Geosci. Workshop, 1992, vol. 1, pp. 147-149.
43. C.-I. Chang, "An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis," IEEE Trans. Inf. Theory, vol. 46, no. 5, pp. 1927-1932, Aug. 2000.
44. C. L. Lawson and R. J. Hanson, Solving Least Squares Problems. Philadelphia, PA, USA: SIAM, 1995.
45. G. Swayze, "The hydrothermal and structural history of the cuprite mining district, southwestern Nevada: An integrated geological and geophysical approach," Ph.D. dissertation, Univ. Colorado, Boulder, CO, USA, 1997.
46. L. Alparone, B. Alazzi, S. Barnoti, A. Garzelli et al., "Multispectral and panchromatic data fusion assessment without reference," Photogramm. Eng. Remote Sens., vol. 74, no. 2, pp. 193-200, 2008. (Pubitemid 351224050)
47. Z. Wang and A. C. Bovik, "A universal image quality index," IEEE Signal Process. Lett., vol. 9, no. 3, pp. 81-84, Mar. 2002. (Pubitemid 34554870)
48. L.-P. Zhang, H.-J. Yu, and S.-X. Hu, "Optimal choice of parameters for particle swarm optimization," J. Zhejiang Univ. Sci. A, vol. 6, no. 6, pp. 528-534, 2005. (Pubitemid 40879456)
49. D. Hamby, "A review of techniques for parameter sensitivity analysis of environment models," Environ. Monit. Assess., vol. 32, no. 2, pp. 135-154, 1994.