A Global Multi-Temporal Dataset with STGAN Baseline for Cloud and Cloud Shadow Removal

Morui Zhu, Chang Liu, Chang Liu, Tamás Szirányi, Tamás Szirányi

2023

Abstract

Due to the inevitable contamination of thick clouds and their shadows, satellite images are greatly affected, which significantly reduces the usability of data from satellite images. Therefore, obtaining high-quality image data without cloud contamination in a specific area and at the time we need it is an important issue. To address this problem, we collected a new multi-temporal dataset covering the entire globe, which is used to remove clouds and their shadows. Since generative adversarial networks (GANs) perform well in conditional image synthesis challenges, we utilized a spatial-temporal GAN (STGAN) to eliminate clouds and their shadows in optical satellite images. As a baseline model, STGAN demonstrated outstanding performance in peak signalto-noise ratio (PSNR) and structural similarity index (SSIM), achieving scores of 33.4 and 0.929, respectively. The cloud-free images generated in this work have significant utility for various downstream applications in real-world environments. Dataset is publicly available: https://github.com/zhumorui/SMT-CR

Paper Citation

in Harvard Style

Zhu M., Liu C. and Szirányi T. (2023). A Global Multi-Temporal Dataset with STGAN Baseline for Cloud and Cloud Shadow Removal. In Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE, ISBN 978-989-758-642-2, SciTePress, pages 206-212. DOI: 10.5220/0012039600003497

in Bibtex Style

@conference{improve23,
author={Morui Zhu and Chang Liu and Tamás Szirányi},
title={A Global Multi-Temporal Dataset with STGAN Baseline for Cloud and Cloud Shadow Removal},
booktitle={Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2023},
pages={206-212},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012039600003497},
isbn={978-989-758-642-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - A Global Multi-Temporal Dataset with STGAN Baseline for Cloud and Cloud Shadow Removal
SN - 978-989-758-642-2
AU - Zhu M.
AU - Liu C.
AU - Szirányi T.
PY - 2023
SP - 206
EP - 212
DO - 10.5220/0012039600003497
PB - SciTePress