NDLI: Bayesian fusion of hyperspectral and multispectral images using Gaussian scale mixture prior

Content Provider	IEEE Xplore Digital Library
Author	Yifan Zhang Shaohui Mei Mingyi He
Copyright Year	2011
Description	Author affiliation: School of Electronics and Information, Northwestern Polytechnical University, Shaanxi Key Laboratory of Information Acquisition and Processing, Xi'an, 710129, China (Yifan Zhang; Shaohui Mei; Mingyi He)
Abstract	In this paper, a wavelet-based Bayesian fusion framework is presented, in which a low spatial resolution hyperspectral (HS) image is fused with a high spatial resolution multi-spectral (MS) image by accounting for the joint statistics. Particularly, a zero-mean heavy-tailed model, Gaussian Scale Mixture (GSM) model, is employed as the prior, which is believed to be capable of modelling the distribution of wavelet coefficients more accurately than traditional Gaussian model. To keep the calculations feasible, a practical implementation scheme is presented. The proposed approach is validated by simulation experiments for both general HS and MS image fusion as well as the specific case of pansharpening. The experimental results of the proposed approach are also compared with its counterpart employing a Gaussian prior for performance evaluation.
Starting Page	2531
Ending Page	2534
File Size	689383
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049727
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	GSM Image fusion Spatial resolution Bayesian methods Hyperspectral imaging Noise level multispectral fusion Gaussian Scale Mixture (GSM) hyperspectral
Content Type	Text
Resource Type	Article

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