Using diffusion geometric coordinates for hyperspectral imagery representation

IEEE Geoscience and Remote Sensing Letters

Volumn 6, Issue 4, 2009, Pages 767-771

  He, Jun ^a   Zhang, Lei ^b   Wang, Qing ^b   Li, Zigang ^b  

^a NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^b SOUTHEAST UNIVERSITY   (China)

Author keywords

Diffusion geometric coordinates; Diffusion maps; Hyperspectral imagery; Nonlinear dimension reduction

Indexed keywords

CLASSIFICATION RESULTS; COMPUTATION COMPLEXITY; DIFFUSION MAPS; DIFFUSION OPERATORS; HIGH DIMENSIONAL DATA; HYPERSPECTRAL DATA; HYPERSPECTRAL IMAGERY; HYPERSPECTRAL REMOTE SENSING; INTRINSIC GEOMETRY; LINEAR METHODS; MEMORY REQUIREMENTS; NON-LINEAR MANIFOLD LEARNING; NONLINEAR DIMENSION REDUCTION; PRINCIPLE COMPONENT ANALYSIS; RANDOM WALK; SPATIAL DISTRIBUTION;

REMOTE SENSING;

DIFFUSION;

EID: 70350304576     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2009.2025058     Document Type: Article

References (17)

1. C.M. Bachmann, T. L. Ainsworth, and R. A. Fusina, " Exploitingmanifold geometry in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol.43, no.3, pp. 441-454, Mar. 2005.
2. C. M. Bachmann, T. L. Ainsworth, and R. A. Fusina, "Improved manifold coordinate representations of large-scale hyperspectral scenes," IEEE Trans. Geosci. Remote Sens., vol.44, no.10, pp. 2786-2803, Oct. 2006.
3. A. Mohan, G. Sapiro, and E. Bosch, "Spatially coherent nonlinear dimensionality reduction and segmentation of hyperspectral images," IEEE Geosci. Remote Sens. Lett., vol.4, no.2, pp. 206-210, Apr. 2007.
4. A. A. Green, M. Berman, P. Switzer, and M. D. Craig, "A transformation for ordering multispectral data in terms of image quality with implications for noise removal," IEEE Trans. Geosci. Remote Sens., vol.26, no.1, pp. 65-74, Jan. 1988.
5. J. B. Tenenbaum, V. Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol.290, no.5500, pp. 2319-2323, Dec. 22, 2000.
6. S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol.290, no.5500, pp. 2323-2326, Dec. 22, 2000.
7. M. Belkin and P. Niyogi, "Laplacian eigenmaps for dimensionality reduction and data representation," Neural Comput., vol.15, no.6, pp. 1373- 1396, Jun. 2003.
8. S. Lafon, "Diffusion maps and geometric harmonics," Ph.D. dissertation, Yale Univ., New Haven, CT, 2004.
9. R. R. Coifman, S. Lafon, A. B. Lee, M. Maggioni, B. Nadler, F. Warner, and S. W. Zucker, "Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps," Proc. Nat. Acad. Sci., vol.102, no.21, pp. 7426-7431, May 24, 2005. (Pubitemid 40740988)
10. R. R. Coifman and S. Lafon, "Diffusion maps," Appl. Comput. Harmon. Anal., vol.21, no.1, pp. 25-30, Jul. 2006.
11. R. R. Coifman and M. Maggioni, "Diffusion wavelets," Appl. Comput. Harmon. Anal., vol.21, no.1, pp. 53-94, Jul. 2006.
12. L. K. Saul and S. T. Roweis, "Think globally, fit locally: Unsupervised learning of low dimensional manifolds," J. Mach. Learn. Res., vol.4, no.2, pp. 119-155, Feb. 2003.
13. M. Hein, J. Y. Audibert, and U. von Luxburg, "From graphs to manifoldsweak and strong pointwise consistency of graph Laplacians," in Proc. 18th COLT, 2005, pp. 470-485.
14. S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Wu, "An optimal algorithm for approximate nearest neighbor searching," J. ACM, vol.45, no.6, pp. 891-923, Nov. 1998.
15. [Online]. Available: http://www.ece.purdue.edu/∼biehl/MultiSpec/ documentation.html
16. [Online]. Available: http://aviris.jpl.nasa.gov/html/aviris.instrument. html
17. S. Velasco-Forero and V. Manian, "Improving hyperspectral image classification using spatial preprocessing," IEEE Geosci. Remote Sens. Lett., vol.6, no.2, pp. 297-301, Apr. 2009.