The objective of this article is to evaluate the performance of three pan‐sharpening algorithms (high‐pass filter, principal component analysis and Gram–Schmidt) to increase the spatial resolution of five types of multispectral images and to evaluate the results in terms of color, coherence and spatial sharpness, both qualitatively and quantitatively. A secondary objective is to present an implementation of the aforementioned pan‐sharpening techniques within the open source software R. From a qualitative point of view, pan‐sharpening of images with a high spatial resolution ratio give better results than those whose spatial resolution ratio is 2. According to the quantitative evaluation, there is no pan‐sharpening methodology that obtains optimal results simultaneously for all types of images used. The results of the spectral and spatial ERGAS index vary for four out of the five types of images analyzed. The results show that none of the methods implemented in this work can be considered a priori better than the others. At the same time, this work indicates the importance of both qualitative and quantitative assessment.

中文翻译：

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fusionImage：一个R软件包，用于在开源软件中进行全景图像锐化

本文的目的是评估三种泛锐化算法（高通滤波器，主成分分析和Gram–Schmidt）的性能，以提高五种类型的多光谱图像的空间分辨率，并根据颜色评估结果定性和定量的连续性和空间清晰度。第二个目标是在开源软件R中提供上述泛锐化技术的实现。从定性的角度来看，具有高空间分辨率的图像的泛锐化效果要优于空间分辨率为的图像。 2.根据定量评估，没有针对所有使用的图像同时获得最佳结果的泛锐化方法。光谱和空间ERGAS指数的结果对于所分析的五种图像中的四种图像有所不同。结果表明，这项工作中实现的任何方法都不比其他方法具有先验性。同时，这项工作表明了定性和定量评估的重要性。