{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T15:23:02Z","timestamp":1776525782623,"version":"3.51.2"},"reference-count":50,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T00:00:00Z","timestamp":1566864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB0504202"],"award-info":[{"award-number":["2017YFB0504202"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41622107"],"award-info":[{"award-number":["41622107"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special projects for technological innovation in Hubei","award":["2018ABA078"],"award-info":[{"award-number":["2018ABA078"]}]},{"name":"Open Fund of Key Laboratory of Ministry of Education for Spatial Data Mining and Information Sharing","award":["2018LSDMIS05"],"award-info":[{"award-number":["2018LSDMIS05"]}]},{"name":"Open Fund of the State Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University","award":["18R02"],"award-info":[{"award-number":["18R02"]}]},{"name":"Open fund of Key Laboratory of Agricultural Remote Sensing of the Ministry of Agriculture","award":["20170007"],"award-info":[{"award-number":["20170007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The precise classification of crop types is an important basis of agricultural monitoring and crop protection. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral remote sensing imagery with high spatial resolution has become the ideal data source for the precise classification of crops. For precise classification of crops with a wide variety of classes and varied spectra, the traditional spectral-based classification method has difficulty in mining large-scale spatial information and maintaining the detailed features of the classes. Therefore, a precise crop classification method using spectral-spatial-location fusion based on conditional random fields (SSLF-CRF) for UAV-borne hyperspectral remote sensing imagery is proposed in this paper. The proposed method integrates the spectral information, the spatial context, the spatial features, and the spatial location information in the conditional random field model by the probabilistic potentials, providing complementary information for the crop discrimination from different perspectives. The experimental results obtained with two UAV-borne high spatial resolution hyperspectral images confirm that the proposed method can solve the problems of large-scale spatial information modeling and spectral variability, improving the classification accuracy for each crop type. This method has important significance for the precise classification of crops in hyperspectral remote sensing imagery.<\/jats:p>","DOI":"10.3390\/rs11172011","type":"journal-article","created":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T11:13:30Z","timestamp":1566904410000},"page":"2011","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery"],"prefix":"10.3390","volume":"11","author":[{"given":"Lifei","family":"Wei","sequence":"first","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}]},{"given":"Ming","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}]},{"given":"Yajing","family":"Liang","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}]},{"given":"Ziran","family":"Yuan","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}]},{"given":"Can","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}]},{"given":"Rong","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}]},{"given":"Yiwei","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eja.2018.10.008","article-title":"Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST\u2013PROSAIL model","volume":"102","author":"Huang","year":"2019","journal-title":"Eur. 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