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To better excavate and fully fuse the features in high-resolution remote sensing images, this paper introduces a novel Global and Local Feature Fusion Network, abbreviated as GLF-Net, by incorporating the extensive contextual information and refined fine-grained features. The proposed GLF-Net, devised as an encoder\u2013decoder network, employs the powerful ResNet50 as its baseline model. It incorporates two pivotal components within the encoder phase: a Covariance Attention Module (CAM) and a Local Fine-Grained Extraction Module (LFM). And an additional wavelet self-attention module (WST) is integrated into the decoder stage. The CAM effectively extracts the features of different scales from various stages of the ResNet and then encodes them with graph convolutions. In this way, the proposed GLF-Net model can well capture the global contextual information with both universality and consistency. Additionally, the local feature extraction module refines the feature map by encoding the semantic and spatial information, thereby capturing the local fine-grained features in images. Furthermore, the WST maximizes the synergy between the high-frequency and the low-frequency information, facilitating the fusion of global and local features for better performance in semantic segmentation. The effectiveness of the proposed GLF-Net model is validated through experiments conducted on the ISPRS Potsdam and Vaihingen datasets. The results verify that it can greatly improve segmentation accuracy.<\/jats:p>","DOI":"10.3390\/rs15194649","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T03:44:42Z","timestamp":1695354282000},"page":"4649","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["GLF-Net: A Semantic Segmentation Model Fusing Global and Local Features for High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Wanying","family":"Song","sequence":"first","affiliation":[{"name":"Xi\u2019an Key Laboratory of Network Convergence Communication, School of Communication and Information Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Xinwei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Xi\u2019an Key Laboratory of Network Convergence Communication, School of Communication and Information Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Shiru","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Key Laboratory of Network Convergence Communication, School of Communication and Information Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3459-5079","authenticated-orcid":false,"given":"Yan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alganci, U., Soydas, M., and Sertel, E. 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