{"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/a-tutorial-on-modeling-and-inference-in","title":"A Tutorial on Modeling and Inference in Undirected Graphical Models for Hyperspectral Image Analysis","arxiv_id":"1801.08268","date":"2018-01-25","proceeding":null,"authors":["Utsav B. Gewali","Sildomar T. Monteiro"],"abstract":"Undirected graphical models have been successfully used to jointly model the\nspatial and the spectral dependencies in earth observing hyperspectral images.\nThey produce less noisy, smooth, and spatially coherent land cover maps and\ngive top accuracies on many datasets. Moreover, they can easily be combined\nwith other state-of-the-art approaches, such as deep learning. This has made\nthem an essential tool for remote sensing researchers and practitioners.\nHowever, graphical models have not been easily accessible to the larger remote\nsensing community as they are not discussed in standard remote sensing\ntextbooks and not included in the popular remote sensing software and\ntoolboxes. In this tutorial, we provide a theoretical introduction to Markov\nrandom fields and conditional random fields based spatial-spectral\nclassification for land cover mapping along with a detailed step-by-step\npractical guide on applying these methods using freely available software.\nFurthermore, the discussed methods are benchmarked on four public hyperspectral\ndatasets for a fair comparison among themselves and easy comparison with the\nvast number of methods in literature which use the same datasets. The source\ncode necessary to reproduce all the results in the paper is published on-line\nto make it easier for the readers to apply these techniques to different remote\nsensing problems.","url_abs":"http://arxiv.org/abs/1801.08268v1","url_pdf":"http://arxiv.org/pdf/1801.08268v1.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":"a-tutorial-on-modeling-and-inference-in","repo_url":"https://github.com/UBGewali/tutorial-UGM-hyperspectral","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-tutorial-on-modeling-and-inference-in","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":"hyperspectral-image-analysis","task_name":"Hyperspectral image analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}