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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Bibliographic Details
Published inScientific programming Vol. 2021; pp. 1 - 11
Main Authors Tian, Liang, Zhong, Xiaorou, Chen, Ming
Format Journal Article
LanguageEnglish
Published New York Hindawi 03.11.2021
Hindawi Limited
Subjects
Artificial neural networks
Deep learning
Generative adversarial networks
Image segmentation
Methods
Neural networks
Object recognition
Remote sensing
Semantic segmentation
Semantics
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Summary: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.
ISSN:1058-9244
1875-919X
DOI:10.1155/2021/9491376