{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:14:33Z","timestamp":1771467273797,"version":"3.50.1"},"reference-count":31,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T00:00:00Z","timestamp":1635897600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R&D and Promotion Special Project of Henan Province","award":["212102210104"],"award-info":[{"award-number":["212102210104"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,11,3]]},"abstract":"<jats:p>Accurate remote sensing image segmentation can guide human activities well, but current image semantic segmentation methods cannot meet the high-precision semantic recognition requirements of complex images. In order to further improve the accuracy of remote sensing image semantic segmentation, this paper proposes a new image semantic segmentation method based on Generative Adversarial Network (GAN) and Fully Convolutional Neural Network (FCN). This method constructs a deep semantic segmentation network based on FCN, which can enhance the receptive field of the model. GAN is integrated into FCN semantic segmentation network to synthesize the global image feature information and then accurately segment the complex remote sensing image. Through experiments on a variety of datasets, it can be seen that the proposed method can meet the high-efficiency requirements of complex image semantic segmentation and has good semantic segmentation capabilities.<\/jats:p>","DOI":"10.1155\/2021\/9491376","type":"journal-article","created":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T22:05:05Z","timestamp":1635977105000},"page":"1-11","source":"Crossref","is-referenced-by-count":11,"title":["Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9625-8326","authenticated-orcid":true,"given":"Liang","family":"Tian","sequence":"first","affiliation":[{"name":"Institute of Computer and Information Engineering, Xinxiang University, XinXiang, Henan 453003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9944-539X","authenticated-orcid":true,"given":"Xiaorou","family":"Zhong","sequence":"additional","affiliation":[{"name":"Institute of Computer and Information Engineering, Xinxiang University, XinXiang, Henan 453003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4029-2621","authenticated-orcid":true,"given":"Ming","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Software Engineering, Zhengzhou University of Light Industry, ZhengZhou, Henan 450000, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1515\/phys-2020-0015"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1515\/phys-2019-0092"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.2174\/2352096509999160819163958"},{"issue":"12","key":"4","first-page":"60","article-title":"A fast spectral clustering algorithm for hyperspectral remote sensing images","volume":"1","author":"Y. 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