High-Fidelity Component Substitution Pansharpening by the Fitting of Substitution Data
High-Fidelity Component Substitution Pansharpening by the Fitting of Substitution Data
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DOI：
10.1109/tgrs.2014.2311815
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发表时间：
2014-04
影响因子：
8.2
通讯作者：
Qizhi Xu;Bo Li;Yun Zhang;L. Ding
中科院分区：
文献类型：
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作者：
Qizhi Xu;Bo Li;Yun Zhang;L. Ding
由于替代分量和被替换分量的“平均信息”不同，在分量替代泛锐化中经常会出现光谱失真。本文在完善的CS方法的基础上，采用一种数据拟合方案来提高图像融合中的光谱质量。提出了一种能够对任意图像融合方法进行建模的广义图像融合框架。该框架不是将全色图像的细节信息注入到替代分量中，而是设计了数据拟合策略，在构建替代分量时调整全色图像的均值信息。数据拟合方案包括两次矩阵减法和一次矩阵卷积。该方法实现速度快，有效地避免了频谱失真问题。在大量泛光谱和多光谱图像上的实验结果表明，改进的CS方法在空间保真度和光谱保真度方面都有良好的性能。此外，在大尺寸图像上进行的实验也表明，该方法具有良好的运行时间性能。
Due to the difference of “mean information” between substitution component and substituted component, spectral distortion often occurs in component substitution (CS) pansharpening. In this paper, a data fitting scheme is adopted to improve spectral quality in image fusion based on well-established CS approach. A generalized CS framework that is capable of modeling any CS image fusion method is also presented. In this framework, instead of injecting detail information of panchromatic (Pan) image into substituted component, the data fitting strategy is designed to adjust the mean information of Pan image in the construction of substitution component. The data fitting scheme involves two matrix subtractions and one matrix convolution. It is fast in implementation and is effective to avoid the spectral distortion problem. Experimental results on a large number of Pan and multispectral images show that the improved CS methods have good performance on the spatial and spectral fidelity. Moreover, experiments carried out on large-size images also show an excellent running time performance of the proposed methods.