A New Look at Image Fusion Methods from a Bayesian Perspective
A New Look at Image Fusion Methods from a Bayesian Perspective
复制标题
从贝叶斯角度重新审视图像融合方法
DOI：
10.3390/rs70606828
复制
发表时间：
2015-05
期刊：
影响因子：
5
通讯作者：
Huang Bo
中科院分区：
文献类型：
--
作者：
Zhang Hankui K.;Huang Bo
分量替换(CS)和多分辨率分析(MRA)是扩展通用图像融合(EGIF)框架中用于融合全色(PAN)和多光谱(MS)图像的两个基本类别。尽管方法多样，但关于融合仍有一些悬而未决的问题和相互矛盾的结论。例如，CS方法的空间增强效果是否优于MRA方法？空间增强和光谱保护是否具有竞争力？如何实现Wald等人定义的光谱一致性。在1997年？在它们的定义中，一旦降级到原始分辨率，任何合成图像都应该与原始图像尽可能地相同。为了回答这些问题，本研究首先发现，所有的CS和MRA方法都可以从贝叶斯融合方法中得到，通过调整一个权重参数来平衡空间注入和光谱保持模型的贡献。光谱保持模型假设期望的高分辨率MS图像的高斯分布，其中上采样的低分辨率MS图像包括平均值。空间注入模型假定平移图像和MS图像之间存在线性相关。因此，空间增强依赖于权重参数，而与该方法属于哪一类(即，MRA或CS)无关。然后在贝叶斯融合框架中加入频谱一致性模型，以保证Wald关于任意传感器点扩展函数的频谱一致性。虽然EGIF方法中的光谱保持与空间增强是竞争的，但Wald的光谱一致性特性与空间增强是互补的。我们在QuickBird和WorldView-2卫星获取的卫星图像上进行了实验，验证了我们的分析，发现加入光谱一致性模型后，传统的EGIF方法的性能有了显著的提高。
Component substitution (CS) and multi-resolution analysis (MRA) are the two basic categories in the extended general image fusion (EGIF) framework for fusing panchromatic (Pan) and multispectral (MS) images. Despite of the method diversity, there are some unaddressed questions and contradictory conclusions about fusion. For example, is the spatial enhancement of CS methods better than MRA methods? Is spatial enhancement and spectral preservation competitive? How to achieve spectral consistency defined by Wald et al. in 1997? In their definition any synthetic image should be as identical as possible to the original image once degraded to its original resolution. To answer these questions, this research first finds out that all the CS and MRA methods can be derived from the Bayesian fusion method by adjusting a weight parameter to balance contributions from the spatial injection and spectral preservation models. The spectral preservation model assumes a Gaussian distribution of the desired high-resolution MS images, with the up-sampled low-resolution MS images comprising the mean value. The spatial injection model assumes a linear correlation between Pan and MS images. Thus the spatial enhancement depends on the weight parameter but is irrelevant of which category (i.e., MRA or CS) the method belongs to. This paper then adds a spectral consistency model in the Bayesian fusion framework to guarantee Wald’s spectral consistency with regard to arbitrary sensor point spread function. Although the spectral preservation in the EGIF methods is competitive to spatial enhancement, the Wald’s spectral consistency property is complementary with spatial enhancement. We conducted experiments on satellite images acquired by the QuickBird and WorldView-2 satellites to confirm our analysis, and found that the performance of the traditional EGIF methods improved significantly after adding the spectral consistency model.
登录
查看更多内容
影响因子：
8.2
作者：
Qizhi Xu;Bo Li;Yun Zhang;L. Ding
通讯作者：
Qizhi Xu;Bo Li;Yun Zhang;L. Ding
DOI：
10.1177/030913339902300207
发表时间：
1999-06
期刊：
Progress in Physical Geography
影响因子：
--
作者：
Daniel N.M. Donoghue
通讯作者：
Daniel N.M. Donoghue
DOI：
10.1007/978-0-387-35973-1_1114
发表时间：
2008
期刊：
2008 IEEE/OES 9th Working Conference on Current Measurement Technology
影响因子：
--
作者：
通讯作者：
--
DOI：
10.1016/s0031-3203(04)00103-7
发表时间：
2004-09
期刊：
--
影响因子：
--
作者：
Gonzalo Pajares Martinsanz;J. García
通讯作者：
Gonzalo Pajares Martinsanz;J. García
DOI：
10.1016/j.isprsjprs.2013.11.011
发表时间：
2014-02-01
影响因子：
12.7
作者：
Zhou, Xiran;Liu, Jun;Huang, Huawen
通讯作者：
Huang, Huawen