Journal
INFORMATION SCIENCES
Volume 180, Issue 18, Pages 3434-3443Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2010.05.010
Keywords
Covariance intersection; Image fusion; Expectation maximization; Multi-spectral image; Panchromatic image; Pansharpening
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Funding
- National Natural Science Foundation of China [10674038, 10974039]
- China Scholarship Council [[2007]3020]
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Image fusion of multi-spectral images and panchromatic images has been widely applied to imaging sensors. Multi-spectral images are rich in spectral information whereas panchromatic images have relatively higher spatial resolution. In this paper, we consider the image fusion as an estimation problem, that is to estimate the ideal scene of multi-spectral images at the resolution of panchromatic images. We propose a method of combining the covariance intersection (Cl) principle with the expectation maximization (EM) algorithm to develop a novel image fusion approach. In contrast to other fusion methods, the proposed scheme takes cross-correlation among data sources into account, and thus provides consistent and accurate estimates through convex combinations. Since the covariance information is usually unknown in practice, the EM method is employed to provide a maximum likelihood estimate (MLE) of the covariance matrix. Real multi-spectral and panchromatic images are used to evaluate the effectiveness of the proposed EM-CI method. The proposed algorithm is found to preserve both the spectral information of the multi-spectral image and the high spatial resolution information of the panchromatic image more effectively than the conventional image fusion techniques. (C) 2010 Elsevier Inc. All rights reserved.
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