{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T19:22:03Z","timestamp":1773429723791,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,4,28]],"date-time":"2018-04-28T00:00:00Z","timestamp":1524873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772532"],"award-info":[{"award-number":["61772532"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61472424"],"award-info":[{"award-number":["61472424"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, deep learning-based methods have drawn increasing attention in hyperspectral imagery (HSI) classification, due to their strong nonlinear mapping capability. However, these methods suffer from a time-consuming training process because of many network parameters. In this paper, the concept of broad learning is introduced into HSI classification. Firstly, to make full use of abundant spectral and spatial information of hyperspectral imagery, hierarchical guidance filtering is performing on the original HSI to get its spectral-spatial representation. Then, the class-probability structure is incorporated into the broad learning model to obtain a semi-supervised broad learning version, so that limited labeled samples and many unlabeled samples can be utilized simultaneously. Finally, the connecting weights of broad structure can be easily computed through the ridge regression approximation. Experimental results on three popular hyperspectral imagery datasets demonstrate that the proposed method can achieve better performance than deep learning-based methods and conventional classifiers.<\/jats:p>","DOI":"10.3390\/rs10050685","type":"journal-article","created":{"date-parts":[[2018,4,30]],"date-time":"2018-04-30T03:45:49Z","timestamp":1525059949000},"page":"685","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System"],"prefix":"10.3390","volume":"10","author":[{"given":"Yi","family":"Kong","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Xuesong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Yuhu","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"C. L. Philip","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau 99999, China; also with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6611","DOI":"10.3390\/rs70606611","article-title":"Adjusted spectral matched filter for target detection in hyperspectral imagery","volume":"7","author":"Gao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2506","DOI":"10.1109\/JSTARS.2014.2329474","article-title":"Integrate growing temperature to estimate the nitrogen content of rice plants at the heading stage using hyperspectral imagery","volume":"7","author":"Onoyama","year":"2014","journal-title":"IEEE J. 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