{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:49:37Z","timestamp":1761598177660,"version":"build-2065373602"},"reference-count":81,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,3]],"date-time":"2020-03-03T00:00:00Z","timestamp":1583193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R and D Program of China","award":["2017YFA0603004","2017YFD0301004"],"award-info":[{"award-number":["2017YFA0603004","2017YFD0301004"]}]},{"name":"the Science Foundation of Shandong","award":["ZR2017MD018"],"award-info":[{"award-number":["ZR2017MD018"]}]},{"name":"the Key Research and Development Program of Ningxia","award":["2019BEH03008"],"award-info":[{"award-number":["2019BEH03008"]}]},{"name":"the Open Research Project of the Key Laboratory for Meteorological Disaster Monitoring, Early Warning and Risk Management of Characteristic Agriculture in Arid Regions","award":["CAMF-201701","CAMF-201803"],"award-info":[{"award-number":["CAMF-201701","CAMF-201803"]}]},{"name":"the arid meteorological science research fund project by the Key Open Laboratory of Arid Climate Change and Disaster Reduction of CMA","award":["IAM201801"],"award-info":[{"award-number":["IAM201801"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Improving the accuracy of edge pixel classification is crucial for extracting the winter wheat spatial distribution from remote sensing imagery using convolutional neural networks (CNNs). In this study, we proposed an approach using a partly connected conditional random field model (PCCRF) to refine the classification results of RefineNet, named RefineNet-PCCRF. First, we used an improved RefineNet model to initially segment remote sensing images, followed by obtaining the category probability vectors for each pixel and initial pixel-by-pixel classification result. Second, using manual labels as references, we performed a statistical analysis on the results to select pixels that required optimization. Third, based on prior knowledge, we redefined the pairwise potential energy, used a linear model to connect different levels of potential energies, and used only pixel pairs associated with the selected pixels to build the PCCRF. The trained PCCRF was then used to refine the initial pixel-by-pixel classification result. We used 37 Gaofen-2 images obtained from 2018 to 2019 of a representative Chinese winter wheat region (Tai\u2019an City, China) to create the dataset, employed SegNet and RefineNet as the standard CNNs, and a fully connected conditional random field as the refinement methods to conduct comparison experiments. The RefineNet-PCCRF\u2019s accuracy (94.51%), precision (92.39%), recall (90.98%), and F1-Score (91.68%) were clearly superior than the methods used for comparison. The results also show that the RefineNet-PCCRF improved the accuracy of large-scale winter wheat extraction results using remote sensing imagery.<\/jats:p>","DOI":"10.3390\/rs12050821","type":"journal-article","created":{"date-parts":[[2020,3,3]],"date-time":"2020-03-03T13:06:23Z","timestamp":1583240783000},"page":"821","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Improved Winter Wheat Spatial Distribution Extraction Using A Convolutional Neural Network and Partly Connected Conditional Random Field"],"prefix":"10.3390","volume":"12","author":[{"given":"Shouyi","family":"Wang","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhigang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, 28 Nanli Road, Wuhan 430068, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9891-7762","authenticated-orcid":false,"given":"Chengming","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinghan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shandong taian NO.2 middle school, 6 Hushandong Road, Taian 271000, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongshan","family":"Mu","sequence":"additional","affiliation":[{"name":"South-to-North Water Transfer East route shandong trunk line Co., Ltd., 33399 Jingshidong Road, Jinan 250000, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"South-to-North Water Transfer East route shandong trunk line Co., Ltd., 33399 Jingshidong Road, Jinan 250000, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, Shandong, China"},{"name":"Shandong Technology and Engineering Center for Digital Agriculture, 61 Daizong Road, Taian 271000, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Gao","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, 9 Dengzhuangnan Road, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/S2095-3119(16)61442-9","article-title":"Assessment of the cropland classifications in four global land cover datasets: A case study of Shaanxi Province, China","volume":"16","author":"Chen","year":"2017","journal-title":"J. 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