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Meanwhile, deep neural network-based methods have many network parameters, which take up a lot of memory and time in training and testing. We propose a novel fully convolutional neural network called the Context Feature Enhancement Network (CFENet) to address these issues. CFENet comprises three modules: the spatial fusion module, the focus enhancement module, and the feature decoder module. First, the spatial fusion module aggregates the spatial information of low-level features to obtain buildings\u2019 outline and edge information. Secondly, the focus enhancement module fully aggregates the semantic information of high-level features to filter the information of building-related attribute categories. Finally, the feature decoder module decodes the output of the above two modules to segment the buildings more accurately. In a series of experiments on the WHU Building Dataset and the Massachusetts Building Dataset, our CFENet balances efficiency and accuracy compared to the other four methods we compared, and achieves optimality on all five evaluation metrics: PA, PC, F1, IoU, and FWIoU. This indicates that CFENet can effectively enhance and fuse buildings\u2019 low-level and high-level features, improving building extraction accuracy.<\/jats:p>","DOI":"10.3390\/rs14092276","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T00:30:28Z","timestamp":1652142628000},"page":"2276","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4131-771X","authenticated-orcid":false,"given":"Jinzhi","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9129-534X","authenticated-orcid":false,"given":"Dejun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiqi","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilin","family":"Chen","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology), Wuhan 430205, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5657-5271","authenticated-orcid":false,"given":"Xiaohu","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Shenzhen Polytechnic, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. 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