Non Gaussian models for hyperspectral algorithm design and assessment

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn 3, Issue , 2002, Pages 1664-1666

  Manolakis, Dimitris ^a   Marden, David ^a  

^a MIT LINCOLN LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; IMAGE ANALYSIS; MATHEMATICAL MODELS; MATRIX ALGEBRA; NORMAL DISTRIBUTION; PROBABILITY DENSITY FUNCTION; RADAR IMAGING; VECTORS;

ELLIPTICALLY CONTOURED DISTRIBUTIONS; HYPERSPECTRAL IMAGING DATA; MULTIVARIATE GAUSSIAN DISTRIBUTION;

REMOTE SENSING;

EID: 0036401372     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (4)

1. K. T. Fang, S. Kotz, and K. W. Ng, Symmetric Multivariate and Related Distributions, Chapman and Hall, New York, 1990.
2. M. Rangaswamy, D. Weiner, and A. Ozturk, "Non-gaussian random vector identification using spherically invariant random processes," IEEE Transactions on Aerospace and Electronic Systems, vol. 29, no. 1, pp. 111-124, January 1993.
3. M. Rangaswamy, D. Weiner, and A. Ozturk, "Computer generation of correlated non-gaussian radar clutter," IEEE Transactions on Aerospace and Electronic Systems, vol. 31, no. 1, pp. 106-116, January 1995.
4. D. Marden and D. Manolakis, "Algorithms for hyperspectral imaging data exploitation using non-gaussian elliptically contoured distributions," in Imaging Spectroscopy VIII, S. S. Shen, Ed., Seattle, WA, 2002, SPIE.