Pansharpening fuses the spatial features of a high-resolution panchromatic (PAN) image with the spectral features of a lower-resolution multispectral (MS) image to generate a spatially enriched MS image. Numerous pansharpening strategies have been developed for more than three decades, which forces the analysts who intend to apply pansharpening to choose from various pansharpening techniques. Hence, this study aims to investigate the performances of many conventional and state-of-the-art pansharpening techniques in order to guide the analysts in this regard. To this aim, the spectral and spatial structure fidelity of the pansharpened images produced from a total of 47 pansharpening methods were evaluated qualitatively and quantitatively. The methods examined were from six pansharpening methods categories, including Multiresolution Analysis (MRA)-based, Component Substitution (CS)-based, Colour-Based (CB), Deep Learning (DL)-based, Variational Optimization (VO)-based and hybrid techniques. The methods in the MRA, DL, CB and VO category were found to exhibit the best pansharpening performances; whereas the hybrid and CS-based techniques showed the poorest performances. We believe that the outcomes of this study will guide the analysts who are in the need to apply pansharpening for their applications.

全色锐化将高分辨率全色 (PAN) 图像的空间特征与低分辨率多光谱 (MS) 图像的光谱特征融合，以生成空间丰富的 MS 图像。三十多年来，已经开发了许多全色锐化策略，这迫使打算应用全色锐化的分析人员从各种全色锐化技术中进行选择。因此，本研究旨在调查许多传统和最先进的全色锐化技术的性能，以在这方面指导分析师。为此，对总共 47 种全色锐化方法产生的全色锐化图像的光谱和空间结构保真度进行了定性和定量评估。检查的方法来自六个全色锐化方法类别，包括基于多分辨率分析 (MRA)、基于组件替换 (CS)、基于颜色 (CB)、基于深度学习 (DL)、基于变分优化 (VO) 和混合技术。发现 MRA、DL、CB 和 VO 类别中的方法表现出最佳的全色锐化性能；而混合和基于 CS 的技术表现出最差的性能。我们相信，这项研究的结果将指导需要在其应用程序中应用全色锐化的分析师。