{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T02:06:58Z","timestamp":1773022018582,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T00:00:00Z","timestamp":1694131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41971423"],"award-info":[{"award-number":["41971423"]}]},{"name":"National Natural Science Foundation of China","award":["31972951"],"award-info":[{"award-number":["31972951"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning has gained widespread interest in the task of building semantic segmentation modelling using remote sensing images; however, neural network models require a large number of training samples to achieve better classification performance, and the models are more sensitive to error patches in the training samples. The training samples obtained in semi-supervised classification methods need less reliable weakly labelled samples, but current semi-supervised classification research puts the generated weak samples directly into the model for applications, with less consideration of the impact of the accuracy and quality improvement of the weak samples on the subsequent model classification. Therefore, to address the problem of generating and optimising the quality of weak samples from training data in deep learning, this paper proposes a semi-supervised building classification framework. Firstly, based on the test results of the remote sensing image segmentation model and the unsupervised classification results of LiDAR point cloud data, this paper quickly generates weak image samples of buildings. Secondly, in order to improve the quality of the spots of the weak samples, an iterative optimisation strategy of the weak samples is proposed to compare and analyse the weak samples with the real samples and extract the accurate samples from the weak samples. Finally, the real samples, the weak samples, and the optimised weak samples are input into the semantic segmentation model of buildings for accuracy evaluation and analysis. The effectiveness of this paper\u2019s approach was experimentally verified on two different building datasets, and the optimised weak samples improved by 1.9% and 0.6%, respectively, in the test accuracy mIoU compared to the initial weak samples. The results demonstrate that the semi-supervised classification framework proposed in this paper can be used to alleviate the model\u2019s demand for a large number of real-labelled samples while improving the ability to utilise weak samples, and it can be used as an alternative to fully supervised classification methods in deep learning model applications that require a large number of training samples.<\/jats:p>","DOI":"10.3390\/rs15184432","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T09:09:21Z","timestamp":1694423361000},"page":"4432","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3317-6518","authenticated-orcid":false,"given":"Yanjun","family":"Wang","sequence":"first","affiliation":[{"name":"National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China"},{"name":"School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China"}]},{"given":"Yunhao","family":"Lin","sequence":"additional","affiliation":[{"name":"National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China"},{"name":"School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China"}]},{"given":"Huiqing","family":"Huang","sequence":"additional","affiliation":[{"name":"The Third Surveying and Mapping Institute of Hunan Province, Changsha 410118, China"},{"name":"Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410118, China"}]},{"given":"Shuhan","family":"Wang","sequence":"additional","affiliation":[{"name":"National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China"},{"name":"School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China"}]},{"given":"Shicheng","family":"Wen","sequence":"additional","affiliation":[{"name":"The Second Survey and Mapping Institute of Hunan Province, Changsha 410118, China"},{"name":"Hunan Provincial Natural Resources Survey and Monitoring Center, Changsha 410118, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9820-7533","authenticated-orcid":false,"given":"Hengfan","family":"Cai","sequence":"additional","affiliation":[{"name":"National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China"},{"name":"School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kwak, G.H., Park, C.W., Lee, K.D., Na, S.I., Ahn, H.Y., and Park, N.W. 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