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Proceedings Paper

Hyperspectral image segmentation, deblurring, and spectral analysis for material identification
Author(s): Fang Li; Michael K. Ng; Robert Plemmons; Sudhakar Prasad; Qiang Zhang
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Paper Abstract

An important aspect of spectral image analysis is identification of materials present in the object or scene being imaged. Enabling technologies include image enhancement, segmentation and spectral trace recovery. Since multi-spectral or hyperspectral imagery is generally low resolution, it is possible for pixels in the image to contain several materials. Also, noise and blur can present significant data analysis problems. In this paper, we first describe a variational fuzzy segmentation model coupled with a denoising/deblurring model for material identification. A statistical moving average method for segmentation is also described. These new approaches are then tested and compared on hyperspectral images associated with space object material identification.

Paper Details

Date Published: 15 April 2010
PDF: 12 pages
Proc. SPIE 7701, Visual Information Processing XIX, 770103 (15 April 2010); doi: 10.1117/12.850121
Show Author Affiliations
Fang Li, East China Normal Univ. (China)
Michael K. Ng, Hong Kong Baptist Univ. (Hong Kong, China)
Robert Plemmons, Wake Forest Univ. (United States)
Sudhakar Prasad, The Univ. of New Mexico (United States)
Qiang Zhang, Wake Forest Univ. (United States)


Published in SPIE Proceedings Vol. 7701:
Visual Information Processing XIX
Zia-ur Rahman; Stephen E. Reichenbach; Mark A. Neifeld, Editor(s)

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