NSSC OpenIR
A new deep learning method for roads recognition from SAR images
Alternative Title20204109338002
Chen, Hua1,2,3; Guo, Wei1,2; Yan, Jing-Wen4; Zhuo, Wen-Hao4; Wu, Liang-Bin5
Source PublicationJilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)
2020
Volume50Issue:5Pages:1778-1787
DOI10.13229/j.cnki.jdxbgxb20190490
ISSN16715497
Language中文
KeywordConvolution - Deep learning - Image enhancement - Learning systems - Neural networks - Remote sensing - Roads and streets - Support vector machines - Synthetic aperture radar Activation functions - Convolution neural network - Learning methods - Overall characteristics - Recognition accuracy - Remote sensing images - Road recognition - Synthetic aperture radar (SAR) images
AbstractDeep learning is an effective technical method to enhance the recognition accuracy of remote sensing image target. To solve the problem of the complicated steps in road recognition from Synthetic Aperture Radar(SAR) images, this paper proposes a SAR image road recognition method based on deep learning. First, based on the traditional Full Convolution Neural Network(FCNN), a New Convolution Neural Network(NCNN) is constructed by revising the activation function to effectively alleviate the loss of road information. Then, the NCNN is applied to road recognition experiments of simulated SAR images and real SAR images with self-constructed road label sets to improve the robustness of the proposed method. The experimental results show that the NCNN can be used to identify the overall characteristics of the road with higher accuracy and better reliability in comparison with the Support Vector Machine(SVM), traditional FCNN and other algorithms. © 2020, Jilin University Press. All right reserved.
Indexed ByEI
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Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7473
Collection中国科学院国家空间科学中心
Affiliation1.Key Laboratory of Microwave Remote Sensing, Chinese Academy of Sciences, Beijing; 100190, China;
2.National Space Science Center, Chinese Academy of Sciences, Beijing; 100190, China;
3.Graduate School of University of Chinese Academy of Sciences, Beijing; 100049, China;
4.College of Engineering, Shantou University, Shantou; 515063, China;
5.Radar and Electronic Equipment Research Institute of China Aviation, Wuxi; 214063, China
Recommended Citation
GB/T 7714
Chen, Hua,Guo, Wei,Yan, Jing-Wen,et al. A new deep learning method for roads recognition from SAR images[J]. Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition),2020,50(5):1778-1787.
APA Chen, Hua,Guo, Wei,Yan, Jing-Wen,Zhuo, Wen-Hao,&Wu, Liang-Bin.(2020).A new deep learning method for roads recognition from SAR images.Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition),50(5),1778-1787.
MLA Chen, Hua,et al."A new deep learning method for roads recognition from SAR images".Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) 50.5(2020):1778-1787.
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