{"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-net-an-end-to-end-cloud-detection","title":"Cloud-Net: An end-to-end Cloud Detection Algorithm for Landsat 8 Imagery","arxiv_id":"1901.10077","date":"2019-01-29","proceeding":"Conference: 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2019 1","authors":["Sorour Mohajerani","Parvaneh Saeedi"],"abstract":"Cloud detection in satellite images is an important first-step in many remote\nsensing applications. This problem is more challenging when only a limited\nnumber of spectral bands are available. To address this problem, a deep\nlearning-based algorithm is proposed in this paper. This algorithm consists of\na Fully Convolutional Network (FCN) that is trained by multiple patches of\nLandsat 8 images. This network, which is called Cloud-Net, is capable of\ncapturing global and local cloud features in an image using its convolutional\nblocks. Since the proposed method is an end-to-end solution, no complicated\npre-processing step is required. Our experimental results prove that the\nproposed method outperforms the state-of-the-art method over a benchmark\ndataset by 8.7\\% in Jaccard Index.","url_abs":"http://arxiv.org/abs/1901.10077v1","url_pdf":"http://arxiv.org/pdf/1901.10077v1.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":"cloud-net-an-end-to-end-cloud-detection","repo_url":"https://github.com/SorourMo/38-Cloud-A-Cloud-Segmentation-Dataset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"cloud-net-an-end-to-end-cloud-detection","repo_url":"https://github.com/SorourMo/Cloud-Net-A-semantic-segmentation-CNN-for-cloud-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"cloud-net-an-end-to-end-cloud-detection","repo_url":"https://github.com/dfrisinghelli/pysegcnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"cloud-detection","task_name":"Cloud Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-38-cloud","task":"Semantic Segmentation","dataset":"38-Cloud","model":"Cloud-Net","rank_in_archive_order":2,"of":2,"metrics":{"Jaccard (Mean)":"87.32"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.10077","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}