Sparse demixing of hyperspectral images

IEEE Transactions on Image Processing

Volumn 21, Issue 1, 2012, Pages 219-228

  Greer, John B ^a  

^a National Geospatial Intelligence Agency   (United States)

Author keywords

Dictionary learning; hyperspectral imaging; linear mixture model (LMM); sparse reconstruction

Indexed keywords

AIRBORNE VISIBLE/INFRARED IMAGING SPECTROMETERS; CONSTRAINED LEAST SQUARES; DE-MIXING; DICTIONARY LEARNING; ENDMEMBERS; HIGH-DIMENSIONAL; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGING; IMAGE SPECTRA; LINEAR MIXTURE MODELS; ORTHOGONAL MATCHING PURSUIT; SPARSE RECONSTRUCTION; SPECTRAL LIBRARIES;

IMAGE PROCESSING; MATHEMATICAL MODELS;

IMAGE MATCHING;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; COLORIMETRY; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SPECTROSCOPY;

ALGORITHMS; COLORIMETRY; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SPECTRUM ANALYSIS;

EID: 84255178230     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2011.2160189     Document Type: Article

References (34)

1. M. J. Abrams and R. P. Ashley, Alteration mapping using multispectral images Cuprite Mining District, Esmeralda County, NV, USGS Open File Rep. 80-367, 1980.
2. M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation," IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4311-4322, Nov. 2006. (Pubitemid 44637761)
3. R. W. Basedow, D. C. Carmer, and M. E. Anderson, "HYDICE system: Implementation and performance," in Proc. SPIE, M. R. Descour, J. M. Mooney, D. L. Perry, and L. R. Illing, Eds., 1995, vol. 2480, pp. 258-267.
4. J. W. Boardman, "Analysis, understanding and visualization of hyperspectral data as convex sets in n-space," in Proc. SPIE, M. R. Descour, J. M. Mooney, D. L. Perry, and L. R. Illing, Eds., 1995, vol. 2480, pp. 14-22.
5. A. R. Boisvert, P. V. Villeneuve, and A. D. Stocker, "Endmember finding and spectral unmixing using least-angle regression," in Proc. SPIE, S. S. Shen and P. E. Lewis, Eds., 2010, vol. 7695, p. 7 6951N.
6. J. H. Bowles and D. B. Gillis, "An optical real-time adaptive spectral identification system (ORASIS)," in Hyperspectral Data Exploitation: Theory and Applications, C.-I. Change, Ed. Hoboken, NJ: Wiley, 2007.
7. E. J. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inform. Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006. (Pubitemid 43174093)
8. T.-H. Chan, C.-Y. Chi, Y. Huang, and W.-K. Ma, "A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing," IEEE Trans. Signal Process., vol. 57, no. 11, pp. 4418-4432, Nov. 2009.
9. R. N. Clark, G. A. Swayze, and A. Gallagher, "Mapping minerals with imaging spectroscopy," USGS Office of Mineral Resources Bulletin 2039 1993, pp. 141-150 [Online]. Available: http://speclab.cr.usgs.gov
10. T. Cocks, R. Jenssen, A. Stewart, I. Wilson, and T. Shields, "The HyMap airborne hyperspectral sensor: The system, calibration and performance," in Proc. 1st EARSEL Workshop Imag. Spectrosc., Zurich, Switzerland, Oct. 1998, pp. 37-42.
11. N. Dobigeon, S. Moussaoui, M. Coulon, J.-Y. Tourneret, and A. O. Hero, "Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery," IEEE Trans. Signal Process., vol. 57, no. 11, pp. 4355-4368, Nov. 2009.
12. D. L. Donoho and J. Tanner, "Sparse non-negative solutions of underdetermined linear equations by linear programming," in Proc. Nat. Acad. Sci., Jul. 2005, vol. 102, no. 27, pp. 9446-9451. (Pubitemid 40981701)
13. O. Duran and M. Petrou, "Spectral unmixing with negative and superunity abundances for subpixel anomaly detection," IEEE Geosci. Remote Sens. Lett., vol. 6, no. 1, pp. 152-156, Jan. 2009.
14. K. Engan, S. O. Aase, and J. H. Husy, "Method of optimal directions for frame design," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 1999, vol. 5, pp. 2443-2446.
15. S. Moussaoui, H. Hauksdóttir, F. Schmidt, C. Jutten, J. Chanussot, D. Brie, S. Douté, and J. A. Benediktsson, "On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation," Neurocomputing, vol. 71, no. 10-12, pp. 2194-2208, Jun. 2008.
16. J.-J. Fuchs, "On sparse representations in arbitrary redundant bases," IEEE Trans. Inf. Theory, vol. 50, no. 6, pp. 1341-1344, Jun. 2004.
17. 1unmixing and its application to hyperspectral image enhancement," in Proc. SPIE, S. S. Shen and P. E. Lewis, Eds., 2009, vol. 7334, pp. 73 341M-1-73 341M-9.
18. S. Jia and Y. Qian, "Constrained nonnegative matrix factorization for hyperspectral unmixing," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 1, pt. 1, pp. 161-173, Jan. 2009.
19. N. Keshava and J. F. Mustard, "Spectral unmixing," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 44-57, Jan. 2002. (Pubitemid 34237207)
20. Jet Propulsion Laboratory AVIRIS. Pasadena, CA [Online]. Available: http://aviris.jpl.nasa.gov
21. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, "Supervised dictionary learning," in Proc. Adv. NIPS, 2008, pp. 1033-1040.
22. S. Mallat and Z. Zhang, "Matching pursuits with time-frequency dictionaries," IEEE Trans. Signal Process., vol. 41, no. 12, pp. 3397-3415, Dec. 1993.
23. S. G. Mallat, A Wavelet Tour of Signal Processing the Sparse Way. New York: Academic, 2008.
24. J. Nascimento and J. Bioucas-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)
25. 1 minimization and linear complementary programming," in Proc. SPIE, S. S. Shen and P. E. Lewis, Eds., 2010, vol. 7695, p. 76 951M.
26. J. Nocedal and S. J. Wright, Numerical Optimization, ser. Springer Series in Operations Research and Fincancial Engineering, 2nd ed. New York: Springer-Verlag, 2006.
27. R. G. Resmini, M. E. Kappus, W. S. Aldrich, J. C. Harsanyi, and M. Anderson, "Mineral mapping with hyperspectral digital imagery collection experiment (HYDICE) sensor data at Cuprite, Nevada, U.S.A," Int. J. Remote Sens., vol. 18, no. 7, pp. 1553-1570, 1997.
28. S. A. Robila and L. G. Maciak, "Considerations on parallelizing nonnegative matrix factorization for hyperspectral data unmixing," IEEE Geosci. Remote Sens. Lett., vol. 6, no. 1, pp. 57-61, Jan. 2009.
29. F. Schmidt, A. Schmidt, E. Trèguier,M. Guiheneuf, S. Moussaoui, and N. Dobigeon, "Implementation strategies for hyperspectral unmixing using Bayesian source separation," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4003-4013, Nov. 2010.
30. J. L. Silván-Cárdenas and L. Wang, "Fully constrained linear spectral unmixing: Analytic solution using fuzzy sets," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 3992-4002, Nov. 2010.
31. J. A. Tropp, "Greed is good: Algorithmic results for sparse approximation," IEEE Trans. Inf. Theory, vol. 50, no. 10, pp. 2231-2242, Oct. 2004.
32. J. A. Tropp, "Just relax: Convex programming methods for identifying sparse signals in noise," IEEE Trans. Inf. Theory, vol. 52, no. 3, pp. 1030-1051, Mar. 2006. (Pubitemid 46444890)
33. J. Verrelst, J. G. P. W. Clevers, and M. E. Schaepman, "Merging the Minnaert-k parameter with spectral unmixing to map forest heterogeneity with CHRIS/PROBAdata," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4014-4022, Nov. 2010.
34. M. E. Winter, "N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data," in Proc. SPIE, M. R. Descour and S. S. Shen, Eds., 1999, vol. 3753, pp. 266-275.