A kernel spatial complexity-based nonlinear unmixing method of hyperspectral imagery

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6330 LNBI, Issue PART 3, 2010, Pages 451-458

  Wu, Xiaoming ^a   Li, Xiaorun ^a   Zhao, Liaoying ^b  

^a ZHEJIANG UNIVERSITY   (China)

^b HANGZHOU DIANZI UNIVERSITY   (China)

Author keywords

kernel function; nonnegative matrix factorization; spatial complexity; spectral unmixing

Indexed keywords

EXCELLENT PERFORMANCE; FEATURE SPACE; HIGH ORDER; HIGH-DIMENSIONAL FEATURE SPACE; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL; HYPERSPECTRAL IMAGERY; HYPERSPECTRAL UNMIXING; INPUT DATAS; KERNEL FUNCTION; MIXED DATA; MIXED PIXEL; NEW MODEL; NON-LINEAR UNMIXING; NONLINEAR MAPPINGS; NONNEGATIVE MATRIX FACTORIZATION; SIMULATED DATA; SPATIAL COMPLEXITY; SPATIAL CORRELATIONS; SPECTRAL DATA; SPECTRAL UNMIXING;

COMPUTER SIMULATION; FACTORIZATION; INTELLIGENT COMPUTING; REMOTE SENSING;

MATRIX ALGEBRA;

EID: 78649546416     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15615-1_53     Document Type: Conference Paper

References (13)

1. Bo, W., Liangpei, Z., Pingxiang, L.: Unmixing Hyperspectral Imagery Based on Support Vector Nonlinear Approximating Regression. J. Remote Sensing 10(3), 312-318 (2006)
2. Kwon, H., Nasrabadi, N.M.: Kernel orthogonal subspace projection for hyperspectral signal classification. J. Geoscience and Remote Sensing 43(12), 2952-2962 (2005)
3. Lee, D.D., Seung, H.S.: Algorithms for Non-negative Matrix Factorization. Advances in Neural Information Processing Systems 13(3), 556-562 (2001)
4. Liu, W., Zheng, N., Lu, X.: Nonnegative matrix factorization for visual coding. In: Proceedings of the IEEE interational Conference on Acoustics.Speech, and Signal Processing (ICASSP 2003), vol. 3, pp. 293-296 (2003)
5. Hoyer, P.O.: Nonnegative matrix factorization with sparseness constraints. J. Journal of Machine Learning Research. 5, 1457-1469 (2004)
6. Jia, S.: Unsupervised Hyperspectral Unmixing Theory and Techniques. Ph.D. Dissertation, University of Zhejiang, Hangzhou (2007)
7. Sjohn, S.T., Nello, C.: Kernel methods for pattern analysis. China Machine Press, Beijing (2005)
8. Clark, R.N., Swayze, G.A., Gallagher, A., King, T.V., Calvin, W.M.: The U.S. Geological Survey, Digital Spectral Library: Version 1: 0.2 to 3.0 μm. U. S. Geol. Surv., Washington, DC, Open File Rep., pp. 93-592 (1993)
9. Nascimento, J.M.P., Dias, J.M.B.: Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing 43(4), 898-910 (2005)
10. Chang, C.I., Du, Q.: Estimation of Number of Spectrally Distinct Signal Sources in Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing 42(3), 608-619 (2004)
11. Grant, M., Boy, S.: Matlab software for disciplined convex programming, http://www.stanf-ord.edu/~boyd/-cvx
12. Swayze, G.: The hydrothermal and structural history of the Cuprite Mining District, south-Western Nevada: An integrated geological and geophysical approach. Ph.D. disserta-tion, Univ. Colorado, Boulder (1997)
13. United States Geological Survey, http://speclab.cr.usgs.gov/cuprite.html