{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:49:28Z","timestamp":1761598168938,"version":"build-2065373602"},"reference-count":70,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,6]],"date-time":"2020-02-06T00:00:00Z","timestamp":1580947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"This research was funded by 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 National Key R and D Program of China","award":["2017YFA0603004"],"award-info":[{"award-number":["2017YFA0603004"]}]},{"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 an important aspect of using convolutional neural networks (CNNs) to extract winter wheat spatial distribution information from remote sensing imagery. In this study, we established a method using prior knowledge obtained from statistical analysis to refine CNN classification results, named post-processing CNN (PP-CNN). First, we used an improved RefineNet model to roughly segment remote sensing imagery in order to obtain the initial winter wheat area and the category probability vector for each pixel. Second, we used manual labels as references and performed statistical analysis on the class probability vectors to determine the filtering conditions and select the pixels that required optimization. Third, based on the prior knowledge that winter wheat pixels were internally similar in color, texture, and other aspects, but different from other neighboring land-use types, the filtered pixels were post-processed to improve the classification accuracy. We used 63 Gaofen-2 images obtained from 2017 to 2019 of a representative Chinese winter wheat region (Feicheng, Shandong Province) to create the dataset and employed RefineNet and SegNet as standard CNN and conditional random field (CRF) as post-process methods, respectively, to conduct comparison experiments. PP-CNN\u2019s accuracy (94.4%), precision (93.9%), and recall (94.4%) were clearly superior, demonstrating its advantages for the improved refinement of edge areas during image classification.<\/jats:p>","DOI":"10.3390\/rs12030538","type":"journal-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T03:13:27Z","timestamp":1581045207000},"page":"538","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis"],"prefix":"10.3390","volume":"12","author":[{"given":"Feng","family":"Li","sequence":"first","affiliation":[{"name":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Shandong Provincial Climate Center, NO.12 Wuying Mountain Road, Jinan 250001, China"}]},{"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, China"},{"name":"Shandong Technology and Engineering Center for Digital Agriculture, 61 Daizong Road, Taian 271000, China"}]},{"given":"Wenwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, China"}]},{"given":"Zhigang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, 28 Nanli Road, Wuhan 430068, China"}]},{"given":"Shouyi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, China"}]},{"given":"Genyun","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Ocean and Space Information, China University of Petroleum (East China), Qingdao 266580, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8684-6610","authenticated-orcid":false,"given":"Zhenjie","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Ocean and Space Information, China University of Petroleum (East China), Qingdao 266580, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isprsjprs.2014.04.023","article-title":"Improved maize cultivated area estimation over a large scale combining MODIS\u2013EVI time series data and crop phenological information","volume":"94","author":"Zhang","year":"2014","journal-title":"ISPRS J. 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