{"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-supervised-learning-for-hyperspectral","title":"Deep supervised learning for hyperspectral data classification through convolutional neural networks","arxiv_id":null,"date":"2015-07-26","proceeding":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2015 7","authors":["Konstantinos Makantasis","Konstantinos Karantzalos","Anastasios Doulamis","Nikolaos Doulamis"],"abstract":"Spectral observations along the spectrum in many narrow spectral bands through hyperspectral imaging provides valuable information towards material and object recognition, which can be consider as a classification task. Most of the existing studies and research efforts are following the conventional pattern recognition paradigm, which is based on the construction of complex handcrafted features. However, it is rarely known which features are important for the problem at hand. In contrast to these approaches, we propose a deep learning based classification method that hierarchically constructs high-level features in an automated way. Our method exploits a Convolutional Neural Network to encode pixels' spectral and spatial information and a Multi-Layer Perceptron to conduct the classification task. Experimental results and quantitative validation on widely used datasets showcasing the potential of the developed approach for accurate hyperspectral data classification.","url_abs":"https://doi.org/10.1109/IGARSS.2015.7326945","url_pdf":"https://doi.org/10.1109/IGARSS.2015.7326945","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-supervised-learning-for-hyperspectral","repo_url":"https://github.com/swalpa/Classification-of-Hyperspectral-Image","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"few-shot-image-classification","task_name":"Few-Shot Image Classification"},{"task_slug":"hyperspectral-image-classification","task_name":"Hyperspectral Image Classification"},{"task_slug":"object-recognition","task_name":"Object Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hyperspectral-image-classification-on-indian","task":"Hyperspectral Image Classification","dataset":"Indian Pines","model":"2D-CNN","rank_in_archive_order":13,"of":34,"metrics":{"OA@15perclass":"57.72±1.90"},"uses_additional_data":false},{"leaderboard":"/sota/hyperspectral-image-classification-on-kennedy","task":"Hyperspectral Image Classification","dataset":"Kennedy Space Center","model":"2D-CNN","rank_in_archive_order":10,"of":14,"metrics":{"OA@15perclass":"80.53±1.31"},"uses_additional_data":false},{"leaderboard":"/sota/hyperspectral-image-classification-on-pavia","task":"Hyperspectral Image Classification","dataset":"Pavia University","model":"2D-CNN","rank_in_archive_order":9,"of":33,"metrics":{"OA@15perclass":"77.53±1.50"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}