{"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/algorithms-for-semantic-segmentation-of","title":"Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning","arxiv_id":"1703.06452","date":"2017-03-19","proceeding":null,"authors":["Ronald Kemker","Carl Salvaggio","Christopher Kanan"],"abstract":"Deep convolutional neural networks (DCNNs) have been used to achieve\nstate-of-the-art performance on many computer vision tasks (e.g., object\nrecognition, object detection, semantic segmentation) thanks to a large\nrepository of annotated image data. Large labeled datasets for other sensor\nmodalities, e.g., multispectral imagery (MSI), are not available due to the\nlarge cost and manpower required. In this paper, we adapt state-of-the-art DCNN\nframeworks in computer vision for semantic segmentation for MSI imagery. To\novercome label scarcity for MSI data, we substitute real MSI for generated\nsynthetic MSI in order to initialize a DCNN framework. We evaluate our network\ninitialization scheme on the new RIT-18 dataset that we present in this paper.\nThis dataset contains very-high resolution MSI collected by an unmanned\naircraft system. The models initialized with synthetic imagery were less prone\nto over-fitting and provide a state-of-the-art baseline for future work.","url_abs":"http://arxiv.org/abs/1703.06452v3","url_pdf":"http://arxiv.org/pdf/1703.06452v3.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":"algorithms-for-semantic-segmentation-of","repo_url":"https://github.com/rmkemker/RIT-18","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"dcnn","method_name":"DCNN"}],"datasets_introduced":[{"slug":"rit-18","name":"RIT-18","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}