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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >A Bayesian Restoration Approach for Hyperspectral Images
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A Bayesian Restoration Approach for Hyperspectral Images

机译：高光谱图像的贝叶斯恢复方法

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摘要

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.
机译：在本文中，提出了一种用于多光谱高光谱（HS）图像观测的贝叶斯恢复技术。作为原型问题，我们假设可以使用同一场景的低空间分辨率HS观察和高空间分辨率多光谱（MS）观察。所提出的方法在HS图像上应用了还原，并与MS图像进行了联合融合，考虑了与MS图像的联合统计。恢复基于期望最大化算法，该算法迭代地应用去模糊步骤和去噪步骤。贝叶斯框架允许包括来自MS图像的空间信息。为了保持计算的可行性，提出了一种实用的实现方案。仿真实验验证了该方法的有效性，适用于一般的HS图像恢复以及全貌的特定情况。将该方法的实验结果与纯融合和反卷积结果进行比较，以进行性能评估。

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