Pansharpening refers to the fusion of a multispectral (MS) image and panchromatic (PAN) data aimed at generating an outcome with the same spatial resolution of the PAN data and the spectral resolution of the MS image. In the last 30 years, several approaches to deal with this issue have been proposed. However, the reproducibility of these methods is often limited, making the comparison with the state of the art hard to achieve. Thus, to fill this gap, we propose a new benchmark consisting of recent advances in MS pansharpening. In particular, optimized classical approaches [multiresolution analysis (MRA) and component substitution (CS)] are compared with methods belonging to the third generation of pansharpening, represented by variational optimization-based (VO) and machine learning (ML) techniques. The benchmark is tested on different scenarios (from urban to rural areas) acquired by different commercial sensors [i.e., IKONOS (IK), GeoEye-1 (GE-1), and WorldView-3 (WV-3)]. Both quantitative and qualitative assessments and the computational burden are analyzed in this article, and all of the implementations have been collected in a MATLAB toolbox that is made available to the community.

中文翻译：

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基于多光谱全景锐化最新进展的新基准：使用经典和新兴的全景锐化方法重新探讨全景锐化

全色锐化是指多光谱（MS）图像和全色（PAN）数据的融合，旨在产生具有与PAN数据相同的空间分辨率和MS图像的光谱分辨率的结果。在过去的30年中，已经提出了几种解决该问题的方法。然而，这些方法的可重复性通常受到限制，使得难以与现有技术进行比较。因此，为了填补这一空白，我们提出了一个新的基准，其中包括MS全景锐化的最新进展。尤其是，将经过优化的经典方法[多分辨率分析（MRA）和组件替换（CS）]与属于第三代泛锐化的方法进行了比较，以基于变分优化（VO）和机器学习（ML）的技术为代表。通过不同的商业传感器（即IKONOS（IK），GeoEye-1（GE-1）和WorldView-3（WV-3））获得的不同场景（从城市到农村）进行了基准测试。本文分析了定量和定性评估以及计算负担，并且所有实现已收集在可供社区使用的MATLAB工具箱中。