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In response, this paper introduces a novel deep learning diffusion model, specifically tailored to improve the spatial resolution of the optical products acquired by the Sentinel-3 (S3) satellite. Our framework employs a diffusion probabilistic model, benefiting from the higher spatial resolution of the Sentinel-2 satellite during training via a new multi-modal loss formulation. This ensures consistency with the original S3 images while enhancing the spatial details. Two distinct conditional low-resolution encoders were experimented with, providing insights into their respective contributions to the diffusion process. The efficacy of the proposed model is demonstrated through extensive ablation studies and comparisons with state-of-the-art methods, using both synthetic and real S3 products. The findings indicate that our model successfully improves spatial resolution while maintaining the integrity of the spectral information, contributing to the field of remote sensing single-image super-resolution.<\/jats:p>","DOI":"10.1007\/s00521-024-10573-9","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T19:03:05Z","timestamp":1738609385000},"page":"7121-7143","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-modal consistent loss diffusion model for Sentinel-3 single image super resolution"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3252-1252","authenticated-orcid":false,"given":"Damian","family":"Iba\u00f1ez","sequence":"first","affiliation":[]},{"given":"Ruben","family":"Fernandez-Beltran","sequence":"additional","affiliation":[]},{"given":"Filiberto","family":"Pla","sequence":"additional","affiliation":[]},{"given":"Naoto","family":"Yokoya","sequence":"additional","affiliation":[]},{"given":"Junshi","family":"Xia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,3]]},"reference":[{"key":"10573_CR1","doi-asserted-by":"crossref","unstructured":"Broni-Bediako C, Xia J, Yokoya N (2023) Real-time semantic segmentation: A brief survey & comparative study in remote sensing. arXiv preprint arXiv:2309.06047","DOI":"10.1109\/MGRS.2023.3321258"},{"key":"10573_CR2","doi-asserted-by":"crossref","unstructured":"Kampffmeyer M, Salberg A-B, Jenssen R (2016) Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. 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