{"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/missing-data-reconstruction-in-remote-sensing","title":"Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network","arxiv_id":"1802.08369","date":"2018-02-23","proceeding":null,"authors":["Qiang Zhang","Qiangqiang Yuan","Chao Zeng","Xinghua Li","Yancong Wei"],"abstract":"Because of the internal malfunction of satellite sensors and poor atmospheric\nconditions such as thick cloud, the acquired remote sensing data often suffer\nfrom missing information, i.e., the data usability is greatly reduced. In this\npaper, a novel method of missing information reconstruction in remote sensing\nimages is proposed. The unified spatial-temporal-spectral framework based on a\ndeep convolutional neural network (STS-CNN) employs a unified deep\nconvolutional neural network combined with spatial-temporal-spectral\nsupplementary information. In addition, to address the fact that most methods\ncan only deal with a single missing information reconstruction task, the\nproposed approach can solve three typical missing information reconstruction\ntasks: 1) dead lines in Aqua MODIS band 6; 2) the Landsat ETM+ Scan Line\nCorrector (SLC)-off problem; and 3) thick cloud removal. It should be noted\nthat the proposed model can use multi-source data (spatial, spectral, and\ntemporal) as the input of the unified framework. The results of both simulated\nand real-data experiments demonstrate that the proposed model exhibits high\neffectiveness in the three missing information reconstruction tasks listed\nabove.","url_abs":"http://arxiv.org/abs/1802.08369v1","url_pdf":"http://arxiv.org/pdf/1802.08369v1.pdf","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":[{"paper_slug":"missing-data-reconstruction-in-remote-sensing","repo_url":"https://github.com/WHUQZhang/STS-CNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"cloud-removal","task_name":"Cloud Removal"},{"task_slug":"sts","task_name":"STS"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.08369","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}