{"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/superpixel-contracted-graph-based-learning","title":"Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification","arxiv_id":"1903.06548","date":"2019-03-14","proceeding":null,"authors":["Philip Sellars","Angelica Aviles-Rivero","Carola-Bibiane Schönlieb"],"abstract":"A central problem in hyperspectral image classification is obtaining high\nclassification accuracy when using a limited amount of labelled data. In this\npaper we present a novel graph-based framework, which aims to tackle this\nproblem in the presence of large scale data input. Our approach utilises a\nnovel superpixel method, specifically designed for hyperspectral data, to\ndefine meaningful local regions in an image, which with high probability share\nthe same classification label. We then extract spectral and spatial features\nfrom these regions and use these to produce a contracted weighted\ngraph-representation, where each node represents a region rather than a pixel.\nOur graph is then fed into a graph-based semi-supervised classifier which gives\nthe final classification. We show that using superpixels in a graph\nrepresentation is an effective tool for speeding up graphical classifiers\napplied to hyperspectral images. We demonstrate through exhaustive quantitative\nand qualitative results that our proposed method produces accurate\nclassifications when an incredibly small amount of labelled data is used. We\nshow that our approach mitigates the major drawbacks of existing approaches,\nresulting in our approach outperforming several comparative state-of-the-art\ntechniques.","url_abs":"http://arxiv.org/abs/1903.06548v3","url_pdf":"http://arxiv.org/pdf/1903.06548v3.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":"superpixel-contracted-graph-based-learning","repo_url":"https://github.com/psellcam/Superpixel-Contracted-Graph-Based-Learning-for-Hyperspectral-Image-Classification","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"hyperspectral-image-classification","task_name":"Hyperspectral Image Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"superpixels","task_name":"Superpixels"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}