{"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/deep-learning-for-classification-of-1","title":"Deep Learning for Classification of Hyperspectral Data: A Comparative Review","arxiv_id":"1904.10674","date":"2019-04-24","proceeding":"IEEE Geoscience and Remote Sensing Magazine 2019 6","authors":["Nicolas Audebert","Bertrand Saux","Sébastien Lefèvre"],"abstract":"In recent years, deep learning techniques revolutionized the way remote\nsensing data are processed. Classification of hyperspectral data is no\nexception to the rule, but has intrinsic specificities which make application\nof deep learning less straightforward than with other optical data. This\narticle presents a state of the art of previous machine learning approaches,\nreviews the various deep learning approaches currently proposed for\nhyperspectral classification, and identifies the problems and difficulties\nwhich arise to implement deep neural networks for this task. In particular, the\nissues of spatial and spectral resolution, data volume, and transfer of models\nfrom multimedia images to hyperspectral data are addressed. Additionally, a\ncomparative study of various families of network architectures is provided and\na software toolbox is publicly released to allow experimenting with these\nmethods. 1 This article is intended for both data scientists with interest in\nhyperspectral data and remote sensing experts eager to apply deep learning\ntechniques to their own dataset.","url_abs":"http://arxiv.org/abs/1904.10674v1","url_pdf":"http://arxiv.org/pdf/1904.10674v1.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":"deep-learning-for-classification-of-1","repo_url":"https://github.com/nshaud/DeepHyperX","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"hyperspectral-image-classification","task_name":"Hyperspectral Image Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hyperspectral-image-classification-on-pavia","task":"Hyperspectral Image Classification","dataset":"Pavia University","model":"DeepHyperX 3D  CNN","rank_in_archive_order":32,"of":33,"metrics":{"Overall Accuracy":"96.71"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.10674","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}