{"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/low-shot-learning-for-the-semantic","title":"Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery","arxiv_id":"1803.09824","date":"2018-03-26","proceeding":null,"authors":["Ronald Kemker","Ryan Luu","Christopher Kanan"],"abstract":"Recent advances in computer vision using deep learning with RGB imagery\n(e.g., object recognition and detection) have been made possible thanks to the\ndevelopment of large annotated RGB image datasets. In contrast, multispectral\nimage (MSI) and hyperspectral image (HSI) datasets contain far fewer labeled\nimages, in part due to the wide variety of sensors used. These annotations are\nespecially limited for semantic segmentation, or pixel-wise classification, of\nremote sensing imagery because it is labor intensive to generate image\nannotations. Low-shot learning algorithms can make effective inferences despite\nsmaller amounts of annotated data. In this paper, we study low-shot learning\nusing self-taught feature learning for semantic segmentation. We introduce 1)\nan improved self-taught feature learning framework for HSI and MSI data and 2)\na semi-supervised classification algorithm. When these are combined, they\nachieve state-of-the-art performance on remote sensing datasets that have\nlittle annotated training data available. These low-shot learning frameworks\nwill reduce the manual image annotation burden and improve semantic\nsegmentation performance for remote sensing imagery.","url_abs":"http://arxiv.org/abs/1803.09824v1","url_pdf":"http://arxiv.org/pdf/1803.09824v1.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":"low-shot-learning-for-the-semantic","repo_url":"https://github.com/rmkemker/EarthMapper","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"few-shot-image-classification","task_name":"Few-Shot Image Classification"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"segmentation-of-remote-sensing-imagery","task_name":"Segmentation Of Remote Sensing Imagery"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"the-semantic-segmentation-of-remote-sensing","task_name":"The Semantic Segmentation Of Remote Sensing Imagery"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}