Noise-Resistant Wavelet-Based Bayesian Fusion of Multispectral and Hyperspectral Images
Noise-Resistant Wavelet-Based Bayesian Fusion of Multispectral and Hyperspectral Images
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DOI：
10.1109/tgrs.2009.2017737
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发表时间：
2009-05
影响因子：
8.2
通讯作者：
Yifan Zhang;S. D. Backer;P. Scheunders
中科院分区：
文献类型：
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作者：
Yifan Zhang;S. D. Backer;P. Scheunders
本文提出了一种多光谱（MS）和高光谱（HS）图像的融合技术，以提高后者的空间分辨率。该技术在小波域中工作，并且基于HS图像的贝叶斯估计，假设图像的联合正常模型和HS图像的加性噪声成像模型。在完整的模型中，定义了一个算子，描述了HS图像的空间退化。由于该算子通常不是精确已知的，并且为了减轻求解逆运算（反卷积问题）的负担，先验地执行插值。此外，空间退化的知识仅限于基于图像之间分辨率差异的近似。该技术相比，其对应的图像域和验证的噪声条件。此外，它的性能相比，几个国家的最先进的泛锐化技术，在MS图像成为全色图像的情况下，和MS和HS图像融合技术的文献。
In this paper, a technique is presented for the fusion of multispectral (MS) and hyperspectral (HS) images to enhance the spatial resolution of the latter. The technique works in the wavelet domain and is based on a Bayesian estimation of the HS image, assuming a joint normal model for the images and an additive noise imaging model for the HS image. In the complete model, an operator is defined, describing the spatial degradation of the HS image. Since this operator is, in general, not exactly known and in order to alleviate the burden of solving the inverse operation (a deconvolution problem), an interpolation is performed a priori . Furthermore, the knowledge of the spatial degradation is restricted to an approximation based on the resolution difference between the images. The technique is compared to its counterpart in the image domain and validated for noisy conditions. Furthermore, its performance is compared to several state-of-the-art pansharpening techniques, in the case where the MS image becomes a panchromatic image, and to MS and HS image fusion techniques from the literature.