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A High-Resolution Dataset of Spatiotemporal Fusion for Remote Sensing Images, 2024

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Paper: X. Zhang, L. Xie, S. Li, F. Lei, L. Cao, and X. Li, “Wuhan Dataset: A High-Resolution Dataset of Spatiotemporal Fusion for Remote Sensing Images,” IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5, Art. no 2504305, August, 2024.

Introduction:
Spatiotemporal fusion is a valid way to provide earth observation applications with remote sensing images with both high temporal and high spatial resolution. Along with the advancement of remote sensing technology, many spatiotemporal fusion methods have been proposed in recent years. However, the existing public spatiotemporal fusion datasets are almost all composed of moderate-resolution imaging spectroradiometer (MODIS) and Landsat images, belonging to the medium resolution datasets. The spatial resolution difference between MODIS and Landsat is up to 16 times, resulting in some texture details indeed difficult to predict. Therefore, these datasets are inadequate for assessing the detail recovery ability of spatiotemporal fusion models accurately. To address this problem, a region with abundant features and significant changes at the junction of Hongshan District and Jiangxia District in Wuhan, Hubei Province, China was selected. For the first time, a high resolution dataset consisting of Gaofen (GF) images and Landsat images was constructed, comprising eight pairs of images spanning more than seven years. Seven fusion methods were selected to be tested on the constructed Wuhan dataset, aiming to explore the detail recovery capacity of these methods and their adaptability to different data sources.

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