{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T11:08:30Z","timestamp":1768475310041,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:00:00Z","timestamp":1661731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61901191"],"award-info":[{"award-number":["61901191"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2020LZH005"],"award-info":[{"award-number":["ZR2020LZH005"]}]},{"name":"National Natural Science Foundation of China","award":["2022M713668"],"award-info":[{"award-number":["2022M713668"]}]},{"name":"Shandong Provincial Natural Science Foundation","award":["61901191"],"award-info":[{"award-number":["61901191"]}]},{"name":"Shandong Provincial Natural Science Foundation","award":["ZR2020LZH005"],"award-info":[{"award-number":["ZR2020LZH005"]}]},{"name":"Shandong Provincial Natural Science Foundation","award":["2022M713668"],"award-info":[{"award-number":["2022M713668"]}]},{"name":"China Postdoctoral Science Foundation","award":["61901191"],"award-info":[{"award-number":["61901191"]}]},{"name":"China Postdoctoral Science Foundation","award":["ZR2020LZH005"],"award-info":[{"award-number":["ZR2020LZH005"]}]},{"name":"China Postdoctoral Science Foundation","award":["2022M713668"],"award-info":[{"award-number":["2022M713668"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning has been widely used in remote sensing image segmentation, while a lack of training data remains a significant issue. The few-shot segmentation of remote sensing images refers to the segmenting of novel classes with a few annotated samples. Although the few-shot segmentation of remote sensing images method based on meta-learning can get rid of the dependence on large data training, the generalization ability of the model is still low. This work presents a few-shot segmentation of remote sensing images with a self-supervised background learner to boost the generalization capacity for unseen categories to handle this challenge. The methodology in this paper is divided into two main modules: a meta learner and a background learner. The background learner supervises the feature extractor to learning latent categories in the image background. The meta learner expands on the classic metric learning framework by optimizing feature representation through contrastive learning between target classes and latent classes acquired from the background learner. Experiments on the Vaihingen dataset and the Zurich Summer dataset show that our model has satisfactory in-domain and cross-domain transferring abilities. In addition, broad experimental evaluations on PASCAL-5i and COCO-20i demonstrate that our model outperforms the prior works of few-shot segmentation. Our approach surpassed previous methods by 1.1% with ResNet-101 in a 1-way 5-shot setting.<\/jats:p>","DOI":"10.3390\/rs14174254","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T01:37:55Z","timestamp":1661823475000},"page":"4254","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Semi-Supervised Contrastive Learning for Few-Shot Segmentation of Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4448-2617","authenticated-orcid":false,"given":"Yadang","family":"Chen","sequence":"first","affiliation":[{"name":"Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenchen","family":"Wei","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duolin","family":"Wang","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanjun","family":"Ji","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baozhu","family":"Li","sequence":"additional","affiliation":[{"name":"Internet of Things & Smart City Innovation Platform, Zhuhai Fudan Innovation Institute, Zhuhai 519031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. 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