{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T23:31:48Z","timestamp":1790292708087,"version":"4.1.0"},"reference-count":36,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T00:00:00Z","timestamp":1583452800000},"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":["41871236"],"award-info":[{"award-number":["41871236"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971193"],"award-info":[{"award-number":["41971193"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007847","name":"Natural Science Foundation of Jilin Province","doi-asserted-by":"publisher","award":["20180101020JC"],"award-info":[{"award-number":["20180101020JC"]}],"id":[{"id":"10.13039\/100007847","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Since the result images obtained by deep semantic segmentation neural networks are usually not perfect, especially at object borders, the conditional random field (CRF) method is frequently utilized in the result post-processing stage to obtain the corrected classification result image. The CRF method has achieved many successes in the field of computer vision, but when it is applied to remote sensing images, overcorrection phenomena may occur. This paper proposes an end-to-end and localized post-processing method (ELP) to correct the result images of high-resolution remote sensing image classification methods. ELP has two advantages. (1) End-to-end evaluation: ELP can identify which locations of the result image are highly suspected of having errors without requiring samples. This characteristic allows ELP to be adapted to an end-to-end classification process. (2) Localization: Based on the suspect areas, ELP limits the CRF analysis and update area to a small range and controls the iteration termination condition. This characteristic avoids the overcorrections caused by the global processing of the CRF. In the experiments, ELP is used to correct the classification results obtained by various deep semantic segmentation neural networks. Compared with traditional methods, the proposed method more effectively corrects the classification result and improves classification accuracy.<\/jats:p>","DOI":"10.3390\/rs12050852","type":"journal-article","created":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T09:26:41Z","timestamp":1583486801000},"page":"852","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["An End-to-End and Localized Post-Processing Method for Correcting High-Resolution Remote Sensing Classification Result Images"],"prefix":"10.3390","volume":"12","author":[{"given":"Xin","family":"Pan","sequence":"first","affiliation":[{"name":"School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, China"},{"name":"Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology, Changchun Institute of Technology, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Xu","sequence":"additional","affiliation":[{"name":"Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology, Changchun Institute of Technology, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5892","DOI":"10.1080\/01431161.2019.1584687","article-title":"An object-based and heterogeneous segment filter convolutional neural network for high-resolution remote sensing image classification","volume":"40","author":"Pan","year":"2019","journal-title":"Int. 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