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Project","award":["B08038"],"award-info":[{"award-number":["B08038"]}]},{"name":"Shaanxi Provincial Science and Technology Innovation Team","award":["2013M540735"],"award-info":[{"award-number":["2013M540735"]}]},{"name":"Shaanxi Provincial Science and Technology Innovation Team","award":["61901388"],"award-info":[{"award-number":["61901388"]}]},{"name":"Shaanxi Provincial Science and Technology Innovation Team","award":["61301291"],"award-info":[{"award-number":["61301291"]}]},{"name":"Shaanxi Provincial Science and Technology Innovation Team","award":["61701360"],"award-info":[{"award-number":["61701360"]}]},{"name":"Shaanxi Provincial Science and Technology Innovation Team","award":["B08038"],"award-info":[{"award-number":["B08038"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["2013M540735"],"award-info":[{"award-number":["2013M540735"]}]},{"name":"the Fundamental Research Funds for the Central 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Universities","award":["61701360"],"award-info":[{"award-number":["61701360"]}]},{"name":"the Youth Innovation Team of Shaanxi Universities","award":["B08038"],"award-info":[{"award-number":["B08038"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land-cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. We address the challenges in land-cover segmentation of remote-sensing imagery and complete the following work. Firstly, to tackle the issues of foreground\u2013background imbalance and scale variation, a module based on multi-dilated rate convolution fusion was integrated into a decoder. This module extended the receptive field through multi-dilated convolution, enhancing the model\u2019s capability to capture global features. Secondly, to address the diversity of scenes and background interference, a hybrid attention module based on large-kernel convolution was employed to improve the performance of the decoder. This module, based on a combination of spatial and channel attention mechanisms, enhanced the extraction of contextual information through large-kernel convolution. A convolution kernel selection mechanism was also introduced to dynamically select the convolution kernel of the appropriate receptive field, suppress irrelevant background information, and improve segmentation accuracy. Ablation studies on the Vaihingen and Potsdam datasets demonstrate that our decoder significantly outperforms the baseline in terms of mean intersection over union and mean F1 score, achieving an increase of up to 1.73% and 1.17%, respectively, compared with the baseline. In quantitative comparisons, the accuracy of our improved decoder also surpasses other algorithms in the majority of categories. The results of this paper indicate that our improved decoder achieves significant performance improvement compared with the old decoder in remote-sensing image-segmentation tasks, which verifies its application potential in the field of land-cover segmentation.<\/jats:p>","DOI":"10.3390\/rs16152851","type":"journal-article","created":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T11:28:54Z","timestamp":1722857334000},"page":"2851","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Optimization of Remote-Sensing Image-Segmentation Decoder Based on Multi-Dilation and Large-Kernel Convolution"],"prefix":"10.3390","volume":"16","author":[{"given":"Guohong","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4450-3801","authenticated-orcid":false,"given":"Xianyun","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an 710071, China"},{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China"},{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunsong","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114417","DOI":"10.1016\/j.eswa.2020.114417","article-title":"A Review of Deep Learning Methods for Semantic Segmentation of Remote Sensing Imagery","volume":"169","author":"Yuan","year":"2021","journal-title":"Expert Syst. 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