{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T20:30:09Z","timestamp":1770150609939,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:00:00Z","timestamp":1674864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971279"],"award-info":[{"award-number":["41971279"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42271324"],"award-info":[{"award-number":["42271324"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["BK20221506"],"award-info":[{"award-number":["BK20221506"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["41971279"],"award-info":[{"award-number":["41971279"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["42271324"],"award-info":[{"award-number":["42271324"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["BK20221506"],"award-info":[{"award-number":["BK20221506"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image (HSI) classification has recently been successfully explored by using deep learning (DL) methods. However, DL models rely heavily on a large number of labeled samples, which are laborious to obtain. Therefore, finding a way to efficiently embed DL models in limited labeled samples is a hot topic in the field of HSI classification. In this paper, an active learning-based siamese network (ALSN) is proposed to solve the limited labeled samples problem in HSI classification. First, we designed a dual learning-based siamese network (DLSN), which consists of a contrastive learning module and a classification module. Secondly, in view of the problem that active learning is difficult to effectively sample under the extremely limited labeling cost, we proposed an adversarial uncertainty-based active learning (AUAL) method to query valuable samples, and to promote DLSN to learn a more complete feature distribution by fine-tuning. Finally, an active learning architecture, based on inter-class uncertainty (ICUAL), is proposed to construct a lightweight sample pair training set, fully extracting the inter-class information of sample pairs and improving classification accuracy. Experiments on three generic HSI datasets strongly demonstrated the effectiveness of ALSN for HSI classification, with performance improvements over other related DL methods.<\/jats:p>","DOI":"10.3390\/rs15030752","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T10:19:28Z","timestamp":1675073968000},"page":"752","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Active Learning-Driven Siamese Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiyao","family":"Di","sequence":"first","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6253-2967","authenticated-orcid":false,"given":"Zhaohui","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China"}]},{"given":"Mengxue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Image and Signal Processing (ISP) Group, University of Val\u00e8ncia, 46980 Val\u00e8ncia, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,28]]},"reference":[{"key":"ref_1","first-page":"2875","article-title":"Use of hyperspectral imagery for mapping grape varieties in the Barossa Valley, South Australia","volume":"Volume 6","author":"Lacar","year":"2001","journal-title":"Proceedings of the IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. 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