文献类型：期刊文献

英文题名：Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model

作者：Tian, Liang[1];Zhong, Xiaorou[1];Chen, Ming[2]

第一作者：田亮

通讯作者：Tian, L[1]

机构：[1]Xinxiang Univ, Inst Comp & Informat Engn, Xinxiang 453003, Henan, Peoples R China;[2]Zhengzhou Univ Light Ind, Coll Software Engn, Zhengzhou 450000, Henan, Peoples R China

第一机构：新乡学院计算机与信息工程学院

通讯机构：[1]corresponding author), Xinxiang Univ, Inst Comp & Informat Engn, Xinxiang 453003, Henan, Peoples R China.|[1107118]新乡学院计算机与信息工程学院;[11071]新乡学院;

年份：2021

卷号：2021

外文期刊名：SCIENTIFIC PROGRAMMING

收录：;EI(收录号：20220411489277);Scopus(收录号：2-s2.0-85122979775);WOS:【SCI-EXPANDED(收录号:WOS:000753279200007)】；

基金：This work was supported by Key R&D and Promotion Special Project of Henan Province (no. 212102210104).

语种：英文

外文关键词：Complex networks - Convolutional neural networks - Generative adversarial networks - Image enhancement - Remote sensing - Semantic Web - Semantics

摘要：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.