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

Zhu, Morui and Liu, Chang and Szirányi, Tamás (2023) A Global Multi-Temporal Dataset with STGAN Baseline for Cloud and Cloud Shadow Removal. In: IMPROVE 2023 : Proceedings of the 3rd International Conference on Image Processing and Vision Engineering. SciTePress Science and Technology Publications, Lda, Setubal, pp. 206-212. ISBN 9789897586422 10.5220/0012039600003497

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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 signal-to-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

Item Type: Book Section
Subjects: Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions: Distributed Events Analysis Research Laboratory
SWORD Depositor: MTMT Injector
Depositing User: MTMT Injector
Date Deposited: 16 Jan 2024 10:24
Last Modified: 16 Jan 2024 10:24
URI: https://eprints.sztaki.hu/id/eprint/10650

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