{"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/spectral-spatial-classification-of","title":"Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders","arxiv_id":"1511.02916","date":"2015-11-09","proceeding":null,"authors":["Zhouhan Lin","Yushi Chen","Xing Zhao","Gang Wang"],"abstract":"Hyperspectral image (HSI) classification is a hot topic in the remote sensing\ncommunity. This paper proposes a new framework of spectral-spatial feature\nextraction for HSI classification, in which for the first time the concept of\ndeep learning is introduced. Specifically, the model of autoencoder is\nexploited in our framework to extract various kinds of features. First we\nverify the eligibility of autoencoder by following classical spectral\ninformation based classification and use autoencoders with different depth to\nclassify hyperspectral image. Further in the proposed framework, we combine PCA\non spectral dimension and autoencoder on the other two spatial dimensions to\nextract spectral-spatial information for classification. The experimental\nresults show that this framework achieves the highest classification accuracy\namong all methods, and outperforms classical classifiers such as SVM and\nPCA-based SVM.","url_abs":"http://arxiv.org/abs/1511.02916v1","url_pdf":"http://arxiv.org/pdf/1511.02916v1.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":"spectral-spatial-classification-of","repo_url":"https://github.com/hantek/deeplearn_hsi","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}