Paper Info
Title
Coupled segmentation and denoising/deblurring models for hyperspectral material identification.
Abstract
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 (C) 2010 John Wiley & Sons, Ltd.
Year
DOI
Venue
2012
10.1002/nla.750
NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS
Keywords
Field
DocType
hyperspectral image analysis,segmentation,denoising,deblurring,compressive representation,tensors
Data set,Mathematical optimization,Scale-space segmentation,Pattern recognition,Deblurring,Segmentation,Segmentation-based object categorization,Hyperspectral imaging,Image segmentation,Artificial intelligence,Contextual image classification,Mathematics
Journal
Volume
Issue
ISSN
19
1
1070-5325
Citations 
PageRank 
References 
10
0.61
9
Authors
3
Name
Order
Citations
PageRank
Fang Li11879.99
Micheal K. Ng2100.61
Robert J. Plemmons390872.32