{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cloud-removal-in-remote-sensing-using","title":"Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models","arxiv_id":null,"date":"2023-05-31","proceeding":"Remote Sensing 2023 5","authors":["Xiaohu Zhao","Kebin Jia"],"abstract":"first_page\r\nsettings\r\nOrder Article Reprints\r\nOpen AccessArticle\r\nCloud Removal in Remote Sensing Using Sequential-Based Diffusion Models\r\nby Xiaohu Zhao\r\n[ORCID] and Kebin Jia\r\n* [ORCID]\r\nFaculty of Information Technology, Beijing University of Technology, Beijing 100124, China\r\n*\r\nAuthor to whom correspondence should be addressed.\r\nRemote Sens. 2023, 15(11), 2861; https://doi.org/10.3390/rs15112861\r\nReceived: 11 April 2023 / Revised: 24 May 2023 / Accepted: 26 May 2023 / Published: 31 May 2023\r\n(This article belongs to the Special Issue Advancing Machine Learning for Remote Sensing to Enhance Spatio-Temporal Generalizability)\r\nDownload Browse Figures\r\nReview Reports Versions Notes\r\n\r\nAbstract\r\nThe majority of the optical observations collected via spaceborne optical satellites are corrupted by clouds or haze, restraining further applications of Earth observation; thus, exploring an ideal method for cloud removal is of great concern. In this paper, we propose a novel probabilistic generative model named sequential-based diffusion models (SeqDMs) for the cloud-removal task in a remote sensing domain. The proposed method consists of multi-modal diffusion models (MmDMs) and a sequential-based training and inference strategy (SeqTIS). In particular, MmDMs is a novel diffusion model that reconstructs the reverse process of denosing diffusion probabilistic models (DDPMs) to integrate additional information from auxiliary modalities (e.g., synthetic aperture radar robust to the corruption of clouds) to help the distribution learning of main modality (i.e., optical satellite imagery). In order to consider the information across time, SeqTIS is designed to integrate temporal information across an arbitrary length of both the main modality and auxiliary modality input sequences without retraining the model again. With the help of MmDMs and SeqTIS, SeqDMs have the flexibility to handle an arbitrary length of input sequences, producing significant improvements only with one or two additional input samples and greatly reducing the time cost of model retraining. We evaluate our method on a public real-world dataset SEN12MS-CR-TS for a multi-modal and multi-temporal cloud-removal task. Our extensive experiments and ablation studies demonstrate the superiority of the proposed method on the quality of the reconstructed samples and the flexibility to handle arbitrary length sequences over multiple state-of-the-art cloud removal approaches.","url_abs":"https://www.mdpi.com/2072-4292/15/11/2861","url_pdf":"https://www.mdpi.com/2072-4292/15/11/2861","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"cloud-removal","task_name":"Cloud Removal"},{"task_slug":"earth-observation","task_name":"Earth Observation"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/cloud-removal-on-sen12ms-cr-ts","task":"Cloud Removal","dataset":"SEN12MS-CR-TS","model":"SeqDMs","rank_in_archive_order":1,"of":7,"metrics":{"PSNR":"28.07","RMSE":"0.045","SAM":"12.777","SSIM":"0.827"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}