{"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/hyperspectral-image-classification-with-1","title":"Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network","arxiv_id":"1705.00727","date":"2017-05-01","proceeding":null,"authors":["Xiangyong Cao","Feng Zhou","Lin Xu","Deyu Meng","Zongben Xu","John Paisley"],"abstract":"This paper presents a new supervised classification algorithm for remotely\nsensed hyperspectral image (HSI) which integrates spectral and spatial\ninformation in a unified Bayesian framework. First, we formulate the HSI\nclassification problem from a Bayesian perspective. Then, we adopt a\nconvolutional neural network (CNN) to learn the posterior class distributions\nusing a patch-wise training strategy to better use the spatial information.\nNext, spatial information is further considered by placing a spatial smoothness\nprior on the labels. Finally, we iteratively update the CNN parameters using\nstochastic gradient decent (SGD) and update the class labels of all pixel\nvectors using an alpha-expansion min-cut-based algorithm. Compared with other\nstate-of-the-art methods, the proposed classification method achieves better\nperformance on one synthetic dataset and two benchmark HSI datasets in a number\nof experimental settings.","url_abs":"http://arxiv.org/abs/1705.00727v2","url_pdf":"http://arxiv.org/pdf/1705.00727v2.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":"hyperspectral-image-classification-with-1","repo_url":"https://github.com/xiangyongcao/CNN_HSIC_MRF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","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":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hyperspectral-image-classification-on-indian","task":"Hyperspectral Image Classification","dataset":"Indian Pines","model":"CNN-MRF","rank_in_archive_order":32,"of":34,"metrics":{"Overall Accuracy":"96.12%"},"uses_additional_data":true},{"leaderboard":"/sota/hyperspectral-image-classification-on-pavia","task":"Hyperspectral Image Classification","dataset":"Pavia University","model":"CNN-MRF","rank_in_archive_order":33,"of":33,"metrics":{"Overall Accuracy":"96.18"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1705.00727","atlas_url":"https://app.syntology.ai/?focus=1705.00727","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}