{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:28:51Z","timestamp":1771698531882,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T00:00:00Z","timestamp":1592956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R\\&amp;D Program of China","award":["2018YFB0504900\uff0c 2018YFB0504905"],"award-info":[{"award-number":["2018YFB0504900\uff0c 2018YFB0504905"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20170811160212033, Grant JCYJ20160330163900579, Grant JCYJ20180507183823045, and Grant JCYJ20170413105929681"],"award-info":[{"award-number":["JCYJ20170811160212033, Grant JCYJ20160330163900579, Grant JCYJ20180507183823045, and Grant JCYJ20170413105929681"]}]},{"name":"Research Grant Council of the Hong Kong SAR","award":["CityU 11502115 and CityU 11525716"],"award-info":[{"award-number":["CityU 11502115 and CityU 11525716"]}]},{"name":"National Natural Science Foundation of China (NSFC) Basic Research Program","award":["Project 71671155"],"award-info":[{"award-number":["Project 71671155"]}]},{"DOI":"10.13039\/501100004479","name":"Natural Science Foundation of Jiangxi Province","doi-asserted-by":"publisher","award":["No.20192ACBL21006"],"award-info":[{"award-number":["No.20192ACBL21006"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate hyperspectral image classification has been an important yet challenging task for years. With the recent success of deep learning in various tasks, 2-dimensional (2D)\/3-dimensional (3D) convolutional neural networks (CNNs) have been exploited to capture spectral or spatial information in hyperspectral images. On the other hand, few approaches make use of both spectral and spatial information simultaneously, which is critical to accurate hyperspectral image classification. This paper presents a novel Synergistic Convolutional Neural Network (SyCNN) for accurate hyperspectral image classification. The SyCNN consists of a hybrid module that combines 2D and 3D CNNs in feature learning and a data interaction module that fuses spectral and spatial hyperspectral information. Additionally, it introduces a 3D attention mechanism before the fully-connected layer which helps filter out interfering features and information effectively. Extensive experiments over three public benchmarking datasets show that our proposed SyCNNs clearly outperform state-of-the-art techniques that use 2D\/3D CNNs.<\/jats:p>","DOI":"10.3390\/rs12122033","type":"journal-article","created":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T08:54:50Z","timestamp":1592988890000},"page":"2033","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Synergistic 2D\/3D Convolutional Neural Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"12","author":[{"given":"Xiaofei","family":"Yang","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen 518055, China"},{"name":"Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen 518055, China"}]},{"given":"Xiaofeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen 518055, China"},{"name":"Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen 518055, China"}]},{"given":"Yunming","family":"Ye","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen 518055, China"},{"name":"Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5751-4550","authenticated-orcid":false,"given":"Raymond Y. K.","family":"Lau","sequence":"additional","affiliation":[{"name":"Department of Information Systems, City University of Hong Kong, Hong Kong 999077, China"}]},{"given":"Shijian","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore 628798, Singapore"}]},{"given":"Xutao","family":"Li","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen 518055, China"},{"name":"Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen 518055, China"}]},{"given":"Xiaohui","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, East China Jiaotong University, Nanchang 330000, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,24]]},"reference":[{"key":"ref_1","first-page":"S110","article-title":"Recent Advances in Techniques for Hyperspectral Image Processing","volume":"113","author":"Fauvel","year":"2007","journal-title":"Remote Sens. 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