Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model

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...

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Autores principales:	Liang Tian, Xiaorou Zhong, Ming Chen
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/de9e92ce64cf4c4ca68c9a464062a45f
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id	oai:doaj.org-article:de9e92ce64cf4c4ca68c9a464062a45f
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spelling	oai:doaj.org-article:de9e92ce64cf4c4ca68c9a464062a45f2021-11-15T01:19:18ZSemantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model1875-919X10.1155/2021/9491376https://doaj.org/article/de9e92ce64cf4c4ca68c9a464062a45f2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9491376https://doaj.org/toc/1875-919XAccurate 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.Liang TianXiaorou ZhongMing ChenHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Liang Tian Xiaorou Zhong Ming Chen Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model
description	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.
format	article
author	Liang Tian Xiaorou Zhong Ming Chen
author_facet	Liang Tian Xiaorou Zhong Ming Chen
author_sort	Liang Tian
title	Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model
title_short	Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model
title_full	Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model
title_fullStr	Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model
title_full_unstemmed	Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model
title_sort	semantic segmentation of remote sensing image based on gan and fcn network model
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/de9e92ce64cf4c4ca68c9a464062a45f
work_keys_str_mv	AT liangtian semanticsegmentationofremotesensingimagebasedonganandfcnnetworkmodel AT xiaorouzhong semanticsegmentationofremotesensingimagebasedonganandfcnnetworkmodel AT mingchen semanticsegmentationofremotesensingimagebasedonganandfcnnetworkmodel
_version_	1718429014993928192

Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model

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