Retrieving Historical Images at 10-m Resolution From 1985 to 2015 Through STARS-Net: A Spatiotemporal Attention Referenced Super-Resolution Network

Shuping Xiong, Xiuyuan Zhang, Haoyu Wang, Yichen Lei, Ge Tan, Shihong Du

Abstract: High-resolution (HR) satellite imagery over long time series is crucial for monitoring fine-scale land use and land cover changes. However, existing freely available Earth observation data are limited to medium to coarse resolutions or short time spans. Our goal is to reconstruct 10-m resolution Sentinel-2-like imagery from 1985 to 2015 using Landsat imagery. Unlike previous studies that heavily rely on HR reference images from the same period, we utilize the overlapping Landsat and Sentinel-2 observations to reconstruct 10-m Sentinel-2-like imagery from 1985 to 2015. Specifically, we propose the STARS-Net, which can effectively transfer multiple similar textures from Sentinel-2 to Landsat images, ensuring accurate reconstruction even with significant land changes. The innovation of our method lies in its ability to integrate any number of HR images, without limiting these images to similar times or the same locations, leading to more flexible and accurate reconstructions. As a result, a 10-m resolution dataset of five major China cities from 1985 to 2015 is available at https://figshare.com/s/186e9b25426067e0dcf4 with a five-year interval. Extensive comparative experiments demonstrate that STARS-Net outperforms state-of-the-art super-resolution (SR) methods and the results highlight that STARS-Net exhibits strong robustness in various scenarios, including cloud cover, image missing, and land cover changes. More importantly, our method produces the first historical image retrospect at 10-m resolution, which has a finer resolution compared to Landsat imagery and an important time extend compared with the Sentinel-2 data. Therefore, our dataset can help to facilitate a deeper understanding of terrestrial transformations over the past four decades.