Structured Sparse Method for Hyperspectral Unmixing

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 88, Issue , 2014, Pages 101-118

  Zhu, Feiyun ^a   Wang, Ying ^a   Xiang, Shiming ^a   Fan, Bin ^a   Pan, Chunhong ^a  

^a INSTITUTE OF AUTOMATION   (China)

Author keywords

Hyperspectral image analysis; Hyperspectral Unmixing (HU); Mixed pixel; Nonnegative Matrix Factorization (NMF); Structured Sparse NMF (SS NMF)

Indexed keywords

SPECTROSCOPY;

HYPERSPECTRAL IMAGE ANALYSIS; HYPERSPECTRAL UNMIXING; MIXED PIXEL; NONNEGATIVE MATRIX FACTORIZATION; STRUCTURED SPARSE NMF (SS-NMF);

PIXELS;

DATA SET; GRAPHICAL METHOD; IMAGE ANALYSIS; NOISE; PIXEL; SPECTRAL ANALYSIS;

EID: 84891131750     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2013.11.014     Document Type: Article

References (54)

1. Belkin M., Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems (NIPS) 2001, 585-591. MIT Press.
2. Berry M.W., Browne M., Langville A.N., Pauca V.P., Plemmons R.J. Algorithms and applications for approximate nonnegative matrix factorization. Comput. Statist. Data Anal. 2007, 30(1):155-173.
3. Bioucas-Dias, J.M., 2009. A variable splitting augmented lagrangian approach to linear spectral unmixing. In: Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). pp. 1-4.
4. Bioucas-Dias, et al. Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 2012, 5(2):354-379.
5. Bishop C.M. Pattern Recognition and Machine Learning. Information Science and Statistics 2006, Springer.
6. Cai D., He X., Han J., Huang T.S. Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 2011, 33(8):1548-1560.
7. Chang C.-I., Du Q. Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. (TGRS) 2004, 42(3):608-619.
8. Chang C.-I., Wu C.-C., Liu W., Ouyang Y.C. A new growing method for simplex-based endmember extraction algorithm. IEEE Trans. Geosci. Remote Sens. (TGRS) 2006, 44(10):2804-2819.
9. Chen X., Kim S., Lin Q., Carbonell J.G., Xing E.P. Graph-structured multitask regression and an efficient optimization method for general fused lasso. Comput. Res. Repository (CORR) 2010, abs/1005(3):1-21.
10. Chung F.R.K. Spectral Graph Theory 1996, American Mathematical Society.
11. Cormen T.H., Leiserson C.E., Rivest R.L., Stein C. Introduction to Algorithms 2001, MIT Press. Second ed.
12. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the em algorithm. J. R. Stat. Soc. 1977, 39(1):1-38.
13. Dobigeon N., Moussaoui S., Coulon M., Tourneret J.-Y., Hero A.O. Joint bayesian endmember extraction and linear unmixing for hyperspectral imagery. IEEE Trans. Signal Process (TSP) 2009, 57(11):4355-4368.
14. Donoho D.L. Compressed sensing. IEEE Trans. Inf. Theory 1996, 52(4):1289-1306.
15. Envi-Tutorials, 2013. Envi Classic Vegetation Hyperspectral Analysis. http://www.exelisvis.com/Learn/WhitepapersDetail/TabId/802/PID/2627/TagID/129/TagName/hyperspectralimagery/Default.aspx.
16. Geng X., Xiao Z., Ji L., Zhao Y., Wang F. A gaussian elimination based fast endmember extraction algorithm for hyperspectral imagery. ISPRS J. Photogramm. Remote Sens. 2013, 79(0):211-218.
17. Gersho A., Gray R.M. Vector Quantization and Signal Compression 1991, Kluwer Academic Publishers, Norwell, MA, USA.
18. Heinz D.C., Chang C.-I. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. (TGRS) 2001, 39(3):529-545.
19. Hoyer, P.O., 2002. Non-negative sparse coding. In: IEEE Workshop Neural Networks for Signal Processing. pp. 557-565.
20. Hoyer P.O., Dayan P. Non-negative matrix factorization with sparseness constraints. J. Mach. Learning Res. (JML) 2004, 5(12):1457-1469.
21. Huck, A., Guillaume, M., 2010. Robust hyperspectral data unmixing with spatial and spectral regularized NMF, 1-4.
22. Iordache M.-D., Bioucas-Dias J.M., Plaza A. Sparse unmixing of hyperspectral data. IEEE Trans. Geosci. Remote Sens. (TGRS) 2011, 49(6):2014-2039.
23. Jia S., Qian Y. Spectral and spatial complexity-based hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. (TGRS) 2007, 45(12):3867-3879.
24. Jia S., Qian Y. Constrained nonnegative matrix factorization for hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. (TGRS) 2009, 47(1):161-173.
25. Jolliffe I.T. Principal Component Analysis 2002, Springer, New York, USA. second ed.
26. Keshava N. A survey of spectral unmixing algorithms. Lincoln Lab. J. 2003, 14(1):55-78.
27. Keshava N., Mustard J.F. Spectral unmixing. IEEE Trans. Signal Process. (TSP) 2002, 19(1):44-57.
28. Kim H., Park H. Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM J. Matrix Anal. Appl. 2008, 30(2):713-730.
29. Kivinen J., Warmuth M.K. Additive versus exponentiated gradient updates for linear prediction. J. Inf. Comput. Sci. 1997, 132(1):1-64.
30. Lee D.D., Seung H.S. Learning the parts of objects with nonnegative matrix factorization. Nature 1999, 401(6755):788-791.
31. Lee D.D., Seung H.S. Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems (NIPS) 2000, 556-562. MIT Press.
32. Li, S.Z., Hou, X., Zhang, H., Cheng, Q., 2001. Learning spatially localized, parts-based representation. In: IEEE International Conference on Computer Vision (CVPR). pp. 207-212.
33. Lin C.-J. Projected gradient methods for nonnegative matrix factorization. Neural Comput. 2007, 19(10):2756-2779. MIT Press.
34. Liu X., Xia W., Wang B., Zhang L. An approach based on constrained nonnegative matrix factorization to unmix hyperspectral data. IEEE Trans. Geosci. Remote Sens. (TGRS) 2011, 49(2):757-772.
35. Liu J., Zhang J., Gao Y., Zhang C., Li Z. Enhancing spectral unmixing by local neighborhood weights. IEEE J. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 2012, 5(5):1545-1552.
36. Logothetis N.K., Sheinberg D.L. Visual object recognition. Annu. Rev. Neurosci. 1996, 19(1):577-621.
37. Lu X., Wu H., Yuan Y., Yan P., Li X. Manifold regularized sparse nmf for hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. (TGRS) 2013, 51(5):2815-2826.
38. Martin G., Plaza A. Spatial-spectral preprocessing prior to endmember identification and unmixing of remotely sensed hyperspectral data. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 2012, 5(2):380-395.
39. Miao L., Qi H., Qi H. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization. IEEE Trans. Geosci. Remote Sens. (TGRS) 2007, 45(3):765-777.
40. Nascimento J.M.P., Bioucas-Dias J.M. Hyperspectral unmixing based on mixtures of dirichlet components. IEEE Trans. Geosci. Remote Sens. (TGRS) 2012, 50(3):863-878.
41. Nascimento J.M.P., Dias J.M.B. Vertex component analysis: a fast algorithm to unmix hyperspectral data. IEEE Trans. Geosci. Remote Sens. (TGRS) 2005, 43(4):898-910.
42. Palmer S.E. Hierarchical structure in perceptual representation. Elsevier J. Cognitive Psych. 1977, 9(4):441-474.
43. Pascual-Montano A.D., et al. Nonsmooth nonnegative matrix factorization (nsnmf). IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 2006, 28(3):403-415.
44. Qian Y., Jia S., Zhou J., Robles-Kelly A. Hyperspectral unmixing via sparsity-constrained nonnegative matrix factorization. IEEE Trans. Geosci. Remote Sens. (TGRS) 2011, 49(11):4282-4297.
45. Sandler R., et al. Nonnegative matrix factorization with earth mover's distance metric for image analysis. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 2011, 33(8):1590-1602.
46. Saul, L., Pereira, F., 1997. Aggregate and mixed-order markov models for statistical language processing. In: Conference on Empirical Methods in Natural Language Process. pp. 81-89.
47. Shahnaz F., Berry M.W., Pauca V.P., Plemmons R.J. Document clustering using nonnegative matrix factorization. Elsevier J. Inf. Process. Manage. 2006, 42(2):373-386.
48. Tibshirani R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. 1996, 58(1):267-288.
49. Tits L., Keersmaecker W.D., Somers B., Asner G.P., Farifteh J., Coppin P. Hyperspectral shape-based unmixing to improve intra- and interclass variability for forest and agro-ecosystem monitoring. ISPRS J. Photogramm. Remote Sens. 2012, 74(0):163-174.
50. Wang J., Chang C.-I. Applications of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. (TGRS) 2006, 44(9):2601-2616.
51. Wang N., Du B., Zhang L. An endmember dissimilarity constrained non-negative matrix factorization method for hyperspectral unmixing. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 2013, 6(2):554-569.
52. Winter, M.E., 1999. N-findr: an algorithm for fast autonomous spectral end-member determination in hyperspectral data. In: SPIE Conference Imaging Spectrometry. pp. 266-275.
53. Xiang S., Nie F., Zhang C. Semi-supervised classification via local spline regression. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 2010, 32(11):2039-2053.
54. Xu, W., Liu, X., Gong, Y., 2003. Document clustering based on non-negative matrix factorization. In: International Conference on Research and Development in Information Retrieval (SIGIR). pp. 267-273.