Paper
15 April 2010 Hyperspectral image segmentation, deblurring, and spectral analysis for material identification
Author Affiliations +
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.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fang Li, Michael K. Ng, Robert Plemmons, Sudhakar Prasad, and Qiang Zhang "Hyperspectral image segmentation, deblurring, and spectral analysis for material identification", Proc. SPIE 7701, Visual Information Processing XIX, 770103 (15 April 2010); https://doi.org/10.1117/12.850121
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CITATIONS
Cited by 11 scholarly publications.
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KEYWORDS
Image segmentation

Hubble Space Telescope

Hyperspectral imaging

Spectral models

Data modeling

Fuzzy logic

Image processing algorithms and systems

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