Alparone Luciano, Garzelli Andrea
Department of Information Engineering, University of Florence, 50139 Florence, Italy.
Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.
J Imaging. 2024 Dec 24;11(1):1. doi: 10.3390/jimaging11010001.
The term pansharpening denotes the process by which the geometric resolution of a multiband image is increased by means of a co-registered broadband panchromatic observation of the same scene having greater spatial resolution. Over time, the benchmarking of pansharpening methods has revealed itself to be more challenging than the development of new methods. Their recent proliferation in the literature is mostly due to the lack of a standardized assessment. In this paper, we draw guidelines for correct and fair comparative evaluation of pansharpening methods, focusing on the reproducibility of results and resorting to concepts of meta-analysis. As a major outcome of this study, an improved version of the additive wavelet luminance proportional (AWLP) pansharpening algorithm offers all of the favorable characteristics of an ideal benchmark, namely, performance, speed, absence of adjustable running parameters, reproducibility of results with varying datasets and landscapes, and automatic correction of the path radiance term introduced by the atmosphere. The proposed benchmarking protocol employs the haze-corrected AWLP-H and exploits meta-analysis for cross-comparisons among different experiments. After assessment on five different datasets, it was found to provide reliable and consistent results in ranking different fusion methods.
全色锐化一词指的是通过对具有更高空间分辨率的同一场景进行配准的宽带全色观测来提高多波段图像几何分辨率的过程。随着时间的推移，事实证明全色锐化方法的基准测试比新方法的开发更具挑战性。它们最近在文献中的大量出现主要是由于缺乏标准化评估。在本文中，我们制定了全色锐化方法正确且公平的比较评估指南，重点关注结果的可重复性，并采用元分析的概念。作为本研究的一个主要成果，加法小波亮度比例（AWLP）全色锐化算法的改进版本具备理想基准的所有有利特性，即性能、速度、不存在可调整的运行参数、不同数据集和景观下结果的可重复性，以及对大气引入的路径辐射项的自动校正。所提出的基准测试协议采用经过 haze 校正的 AWLP-H，并利用元分析进行不同实验之间的交叉比较。在对五个不同数据集进行评估后，发现它在对不同融合方法进行排名时能提供可靠且一致的结果。
