{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:34:12Z","timestamp":1775838852764,"version":"3.50.1"},"reference-count":19,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T00:00:00Z","timestamp":1592870400000},"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":["61866016"],"award-info":[{"award-number":["61866016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing","award":["2016WICSIP031"],"award-info":[{"award-number":["2016WICSIP031"]}]},{"name":"ESA\/NRCSS Dragon-4 program","award":["32235"],"award-info":[{"award-number":["32235"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The traditional local binary pattern (LBP, hereinafter we also call it a two-dimensional local binary pattern 2D-LBP) is unable to depict the spectral characteristics of a hyperspectral image (HSI). To cure this deficiency, this paper develops a joint spectral-spatial 2D-LBP feature (J2D-LBP) by averaging three different 2D-LBP features in a three-dimensional hyperspectral data cube. Subsequently, J2D-LBP is added into the Gabor filter-based deep network (GFDN), and then a novel classification method JL-GFDN is proposed. Different from the original GFDN framework, JL-GFDN further fuses the spectral and spatial features together for HSI classification. Three real data sets are adopted to evaluate the effectiveness of JL-GFDN, and the experimental results verify that (i) JL-GFDN has a better classification accuracy than the original GFDN; (ii) J2D-LBP is more effective in HSI classification in comparison with the traditional 2D-LBP.<\/jats:p>","DOI":"10.3390\/rs12122016","type":"journal-article","created":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T07:14:19Z","timestamp":1592982859000},"page":"2016","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["JL-GFDN: A Novel Gabor Filter-Based Deep Network Using Joint Spectral-Spatial Local Binary Pattern for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"12","author":[{"given":"Tao","family":"Zhang","sequence":"first","affiliation":[{"name":"Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang 330108, China"},{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}]},{"given":"Puzhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Division of Geoinformatics, KTH Royal Institute of Technology, 10044 Stockholm, Sweden"}]},{"given":"Weilin","family":"Zhong","sequence":"additional","affiliation":[{"name":"Shanghai Key Lab. of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Zhen","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Optic-Electronic and Communication, Jiangxi Science and Technology Normal University, Nanchang 330013, China"}]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang 330108, China"},{"name":"Key Laboratory of Optic-Electronic and Communication, Jiangxi Science and Technology Normal University, Nanchang 330013, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1109\/JSTARS.2015.2420651","article-title":"Assessment of Spatial\u2013Spectral Feature-Level Fusion for Hyperspectral Target Detection","volume":"8","author":"Kaufman","year":"2015","journal-title":"IEEE J. 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