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首页> 外文期刊>International journal of image and data fusion >Wavelet-based Bayesian fusion of multispectral and hyperspectral images using Gaussian scale mixture model
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Wavelet-based Bayesian fusion of multispectral and hyperspectral images using Gaussian scale mixture model

机译：高斯尺度混合模型基于小波的贝叶斯多光谱和高光谱图像融合

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摘要

In this article, a wavelet-based Bayesian fusion framework is presented, in which a low spatial resolution hyperspectral (HS) image is fused with a high spatial resolution multispectral (MS) image by accounting for the joint statistics. Particularly, a zero-mean heavy-tailed model, Gaussian scale mixture 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.
机译：在本文中，提出了一种基于小波的贝叶斯融合框架，其中通过考虑联合统计，将低空间分辨率高光谱（HS）图像与高空间分辨率多光谱（MS）图像融合。特别地，零均值重尾模型，高斯尺度混合模型被用作先验模型，与传统的高斯模型相比，该模型被认为能够更精确地建模小波系数的分布。为了使计算可行，提出了一种实用的实现方案。通过仿真实验对通用HS和MS图像融合以及全景锐化的具体情况进行了验证。提议的方法的实验结果也与它的对应方法进行了比较，采用高斯先验进行性能评估。

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