{"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-of","title":"Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples","arxiv_id":null,"date":"2020-06-01","proceeding":"Journal of Physics: Conference Series 2020 6","authors":["Lixin Hu","Xiaobo Luo","Yufan Wei"],"abstract":"Aiming at the problem that the manual labeling of samples in the hyperspectral image classification is expensive and laborious, a large number of unlabeled samples are not effectively utilized and the classification results are not ideal. A method which can provide valuable samples and employ convolutional neural network to extract spectral spatial features for classification is proposed. Active learning method is used to construct a valuable training sample set by iteratively selecting the most uncertain samples through support vector machine which performs well in small sample classification, and labeling them. Then the 3D convolutional neural network is used to extract the spectral spatial features of hyperspectral image. The experimental results of the hyperspectral classification on Indian Pines and PaviaU datasets show that the proposed method (3D VS-CNN) is better than traditional classification methods.","url_abs":"http://doi.org/10.1088/1742-6596/1549/5/052011","url_pdf":"https://iopscience.iop.org/article/10.1088/1742-6596/1549/5/052011/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":[],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"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":"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":"3D VS-CNN","rank_in_archive_order":5,"of":34,"metrics":{"OA@15perclass":"83.06±1.04"},"uses_additional_data":false},{"leaderboard":"/sota/hyperspectral-image-classification-on-kennedy","task":"Hyperspectral Image Classification","dataset":"Kennedy Space Center","model":"3D VS-CNN","rank_in_archive_order":11,"of":14,"metrics":{"OA@15perclass":"80.15±0.62"},"uses_additional_data":false},{"leaderboard":"/sota/hyperspectral-image-classification-on-pavia","task":"Hyperspectral Image Classification","dataset":"Pavia University","model":"3D VS-CNN","rank_in_archive_order":8,"of":33,"metrics":{"OA@15perclass":"81.63±1.81"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}