{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:26:56Z","timestamp":1772119616978,"version":"3.50.1"},"reference-count":83,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Second Batch of the \u201cRevealing the List and Appointing Leaders\u201d Scientific and Technological Tackling of Projects in Heilongjiang Province","award":["2021ZXJ05A01-04"],"award-info":[{"award-number":["2021ZXJ05A01-04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Thin clouds in Remote Sensing (RS) imagery can negatively impact subsequent applications. Current Deep Learning (DL) approaches often prioritize information recovery in cloud-covered areas but may not adequately preserve information in cloud-free regions, leading to color distortion, detail loss, and visual artifacts. This study proposes a Sparse Transformer-based Generative Adversarial Network (SpT-GAN) to solve these problems. First, a global enhancement feature extraction module is added to the generator\u2019s top layer to enhance the model\u2019s ability to preserve ground feature information in cloud-free areas. Then, the processed feature map is reconstructed using the sparse transformer-based encoder and decoder with an adaptive threshold filtering mechanism to ensure sparsity. This mechanism enables that the model preserves robust long-range modeling capabilities while disregarding irrelevant details. In addition, inverted residual Fourier transformation blocks are added at each level of the structure to filter redundant information and enhance the quality of the generated cloud-free images. Finally, a composite loss function is created to minimize error in the generated images, resulting in improved resolution and color fidelity. SpT-GAN achieves outstanding results in removing clouds both quantitatively and visually, with Structural Similarity Index (SSIM) values of 98.06% and 92.19% and Peak Signal-to-Noise Ratio (PSNR) values of 36.19 dB and 30.53 dB on the RICE1 and T-Cloud datasets, respectively. On the T-Cloud dataset, especially with more complex cloud components, the superior ability of SpT-GAN to restore ground details is more evident.<\/jats:p>","DOI":"10.3390\/rs16193658","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T10:00:47Z","timestamp":1727690447000},"page":"3658","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Thin Cloud Removal Generative Adversarial Network Based on Sparse Transformer in Remote Sensing Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7021-4831","authenticated-orcid":false,"given":"Jinqi","family":"Han","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9582-519X","authenticated-orcid":false,"given":"Ying","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China"}]},{"given":"Xindan","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1933-8357","authenticated-orcid":false,"given":"Yinghui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Forest Management, School of Forestry, Northeast Forestry University, Harbin 150040, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"D19","DOI":"10.1029\/2003JD004457","article-title":"Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data","volume":"109","author":"Zhang","year":"2004","journal-title":"J. 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