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Coupled segmentation and denoising/deblurring models for hyperspectral material identification
Author(s) -
Li Fang,
Ng Michael K.,
Plemmons Robert J.
Publication year - 2012
Publication title -
numerical linear algebra with applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.02
H-Index - 53
eISSN - 1099-1506
pISSN - 1070-5325
DOI - 10.1002/nla.750
Subject(s) - hyperspectral imaging , deblurring , artificial intelligence , pattern recognition (psychology) , identification (biology) , segmentation , computer science , noise reduction , data set , noise (video) , image segmentation , computer vision , mathematics , image (mathematics) , image processing , image restoration , botany , biology
SUMMARY A crucial aspect of spectral image analysis is the identification of the materials present in the object or scene being imaged and to quantify their abundance in the mixture. An increasingly useful approach to extracting such underlying structure is to employ image classification and object identification techniques to compressively represent the original data cubes by a set of spatially orthogonal bases and a set of spectral signatures. Owing to the increasing quantity of data usually encountered in hyperspectral data sets, effective data compressive representation is an important consideration, and noise and blur can present data analysis problems. In this paper, we develop image segmentation methods for hyperspectral space object material identification. We also couple the segmentation with a hyperspectral image data denoising/deblurring model and propose this method as an alternative to a tensor factorization methods proposed recently for space object material identification. The model provides the segmentation result and the restored image simultaneously. Numerical results show the effectiveness of our proposed combined model in hyperspectral material identification. Copyright © 2010 John Wiley & Sons, Ltd.