{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T18:45:45Z","timestamp":1785955545783,"version":"3.56.0"},"reference-count":37,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:00:00Z","timestamp":1604361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chris H.Q.Ding","award":["61572030"],"award-info":[{"award-number":["61572030"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Image segmentation has made great progress in recent years, but the annotation required for image segmentation is usually expensive, especially for remote sensing images. To solve this problem, we explore semi-supervised learning methods and appropriately utilize a large amount of unlabeled data to improve the performance of remote sensing image segmentation. This paper proposes a method for remote sensing image segmentation based on semi-supervised learning. We first design a Consistency Regularization (CR) training method for semi-supervised training, then employ the new learned model for Average Update of Pseudo-label (AUP), and finally combine pseudo labels and strong labels to train semantic segmentation network. We demonstrate the effectiveness of the proposed method on three remote sensing datasets, achieving better performance without more labeled data. Extensive experiments show that our semi-supervised method can learn the latent information from the unlabeled data to improve the segmentation performance.<\/jats:p>","DOI":"10.3390\/rs12213603","type":"journal-article","created":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T09:09:32Z","timestamp":1604394572000},"page":"3603","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["Semi-Supervised Remote Sensing Image Semantic Segmentation via Consistency Regularization and Average Update of Pseudo-Label"],"prefix":"10.3390","volume":"12","author":[{"given":"Jiaxin","family":"Wang","sequence":"first","affiliation":[{"name":"Key Lab of IC&amp;SP of MOE, School of Computer Science and Technology, Anhui University, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chris","family":"H. Q. Ding","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sibao","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Lab of IC&amp;SP of MOE, School of Computer Science and Technology, Anhui University, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenggang","family":"He","sequence":"additional","affiliation":[{"name":"Key Lab of IC&amp;SP of MOE, School of Computer Science and Technology, Anhui University, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5948-5055","authenticated-orcid":false,"given":"Bin","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Lab of IC&amp;SP of MOE, School of Computer Science and Technology, Anhui University, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6061","DOI":"10.1109\/TGRS.2016.2580643","article-title":"Multiscale and Multifeature Normalized Cut Segmentation for High Spatial Resolution Remote Sensing Imagery","volume":"54","author":"Zhong","year":"2016","journal-title":"IEEE Trans. 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