Effects of dimensionality reduction on the statistical distribution of hyperspectral backgrounds

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 6302, Issue , 2006, Pages

  Rossacci, M ^a   Manolakis, D ^a   Cipar, J ^b   Lockwood, R ^b   Cooley, T ^b   Jacobson, J ^c  

^a MIT LINCOLN LABORATORY   (United States)

^b AIR FORCE RESEARCH LABORATORY   (United States)

^c WRIGHT PATTERSON AIR FORCE BASE   (United States)

Author keywords

Background characterization; Dimensionality reduction; Hyperspectral imaging; Statistical modeling; Target detection

Indexed keywords

BACKGROUND CHARACTERIZATION; DIMENSIONALITY REDUCTION; HYPERSPECTRAL IMAGING; STATISTICAL MODELING; TARGET DETECTION;

IMAGE SEGMENTATION; MATHEMATICAL MODELS; SPECTRUM ANALYSIS; STATISTICAL METHODS;

IMAGING SYSTEMS;

EID: 33750560239     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.680388     Document Type: Conference Paper

References (9)

1. D. Manolakis, M. Rossacci, J. Cipar, R. Lockwood, T. Cooley, and J. Jacobson. "Statistical characterizatiou of natural hyperspectral backgrounds using t-elliptically contoured distributions," in Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery XI, S. S. Shen, ed., 5806, SPIE, (Bellingham, WA), 2005.
2. D. Marden and D. Manolakis, "Modeling hyperspectral imaging data using elliptically contoured distributions," in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, S. S. Shen and P. E. Lewis, eds., SPIE, (Orlando, FL), 2003.
3. D. Marden and D. Manolakis, "Statistical modeling of hyperspectral imaging data arid their applications," Tech. Rep. HTAP-16, Lincoln Laboratory, MIT, Lexington, MA, 2004.
4. R. O. Green et al., "Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS)," Remote Sens. Environ. 65, pp. 227-248, 1998.
5. S. Golden-Adler et al., "FLAASH, A MODTRAN4 atmospheric correction package for hyperspectral data retrievals and simulations," (97-21), JPL, 1998.
6. S. Golden-Adler et al., "Atmospheric correction for short-wave spectral imagery based on MODTRAN4," 3753, SPIE, 1999.
7. M. Matthew et al., "Atmospheric correction of spectral imagery: Evaluation of the FLAASH algorithm with AVIRIS data," in Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery IX, 5093, SPIE, 2003.
8. J. Cipar, "Background spectral library for Fort A.P. Hill, Virginia," SPIE, 2004.
9. D. Manolakis and D, Marden, "Dimensionality reduction of hyperspectral imaging data using local principal component transforms," in Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery X, S. S. Shen, ed., 5425, SPIE, (Bellingham, WA), 2004.