{"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/efficient-statistical-classification-of","title":"Efficient statistical classification of satellite measurements","arxiv_id":"1202.2194","date":"2012-02-10","proceeding":null,"authors":["Peter Mills"],"abstract":"Supervised statistical classification is a vital tool for satellite image\nprocessing. It is useful not only when a discrete result, such as feature\nextraction or surface type, is required, but also for continuum retrievals by\ndividing the quantity of interest into discrete ranges. Because of the high\nresolution of modern satellite instruments and because of the requirement for\nreal-time processing, any algorithm has to be fast to be useful. Here we\ndescribe an algorithm based on kernel estimation called Adaptive Gaussian\nFiltering that incorporates several innovations to produce superior efficiency\nas compared to three other popular methods: k-nearest-neighbour (KNN), Learning\nVector Quantization (LVQ) and Support Vector Machines (SVM). This efficiency is\ngained with no compromises: accuracy is maintained, while estimates of the\nconditional probabilities are returned. These are useful not only to gauge the\naccuracy of an estimate in the absence of its true value, but also to\nre-calibrate a retrieved image and as a proxy for a discretized continuum\nvariable. The algorithm is demonstrated and compared with the other three on a\npair of synthetic test classes and to map the waterways of the Netherlands.\nSoftware may be found at: http://libagf.sourceforge.net.","url_abs":"http://arxiv.org/abs/1202.2194v4","url_pdf":"http://arxiv.org/pdf/1202.2194v4.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":"efficient-statistical-classification-of","repo_url":"https://github.com/tommyod/KDEpy","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"quantization","task_name":"Quantization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}