DocumentCode :
1463155
Title :
A Bayesian Restoration Approach for Hyperspectral Images
Author :
Zhang, Yifan ; Duijster, Arno ; Scheunders, Paul
Author_Institution :
Shaanxi Key Lab. of Inf. Acquisition & Process., Northwestern Polytech. Univ., Xi´´an, China
Volume :
50
Issue :
9
fYear :
2012
Firstpage :
3453
Lastpage :
3462
Abstract :
In this paper, a Bayesian restoration technique for multiple observations of hyperspectral (HS) images is presented. As a prototype problem, we assume that a low-spatial-resolution HS observation and a high-spatial-resolution multispectral (MS) observation of the same scene are available. The proposed approach applies a restoration on the HS image and a joint fusion with the MS image, accounting for the joint statistics with the MS image. The restoration is based on an expectation-maximization algorithm, which applies a deblurring step and a denoising step iteratively. The Bayesian framework allows to include spatial information from the MS image. To keep the calculation feasible, a practical implementation scheme is presented. The proposed approach is validated by simulation experiments for general HS image restoration and for the specific case of pansharpening. The experimental results of the proposed approach are compared with pure fusion and deconvolution results for performance evaluation.
Keywords :
Bayes methods; deconvolution; geophysical image processing; image denoising; image fusion; image restoration; prototypes; Bayesian restoration approach; deblurring step; deconvolution; denoising step; expectation maximization algorithm; high spatial resolution multispectral observation; hyperspectral images; image spatial information; joint MS image fusion; low spatial resolution HS observation; prototype problem; Deconvolution; Estimation; Image restoration; Noise; Noise reduction; Spatial resolution; Bayesian; deconvolution; expectation–maximization (EM); fusion; hyperspectral (HS) image; restoration;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2012.2184122
Filename :
6164260
Link To Document :
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