Abstract

Spatio-temporal fusion (STF) aims at fusing (temporally dense) coarse resolution images and (temporally sparse) fine resolution images to generate image series with adequate temporal and spatial resolution. In the last decade, STF has drawn a lot of attention and many STF methods have been developed. However, to date the STF domain still lacks benchmark datasets, which is a pressing issue that needs to be addressed in order to foster the development of this field. In this review, we provide (for the first time in the literature) a robust benchmark STF dataset that includes three important characteristics: (1) diversity of regions, (2) long timespan, and (3) challenging scenarios. We also provide a survey of highly representative STF techniques, along with a detailed quantitative and qualitative comparison of their performance with our newly presented benchmark dataset. The proposed dataset is public and available online.

中文翻译：

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遥感数据的时空融合：概述和新基准

摘要

时空融合（STF）的目的是融合（暂时密集的）粗分辨率图像和（暂时稀疏的）精细分辨率图像，以生成具有足够时空分辨率的图像序列。在过去的十年中，STF引起了很多关注，并且开发了许多STF方法。但是，迄今为止，STF域仍然缺乏基准数据集，这是一个紧迫的问题，必须进行处理才能促进该领域的发展。在这篇综述中，我们（在文献中第一次）提供了一个强大的基准STF数据集，该数据集包含三个重要特征：（1）区域多样性，（2）长时间跨度和（3）挑战性场景。我们还提供了具有代表性的STF技术的调查，以及我们新提供的基准数据集对其性能的详细定量和定性比较。提议的数据集是公开的，可以在线获取。