Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (5): 1778-1787.doi: 10.13229/j.cnki.jdxbgxb20190490

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A new deep learning method for roads recognition from SAR images

Hua CHEN1,2,3(),Wei GUO1,2(),Jing-wen YAN4,Wen-hao ZHUO4,Liang-bin WU5   

  1. 1.Key Laboratory of Microwave Remote Sensing, Chinese Academy of Sciences, Beijing 0090, 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
  • Received:2019-05-20 Online:2020-09-01 Published:2020-09-16
  • Contact: Wei GUO E-mail:e_chenhua@163.com;guowei@mirslab.cn

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Abstract:

Deep 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.

Key words: information processing technology, synthetic aperture radar image, roads recognition, deep learning, fully convolutional network, max feature map

CLC Number: 

  • TP391.4

Fig.1

Typical frame diagram of fully convolutional network model"

Fig.2

MFM-based convolution neural network framework"

Fig.3

M-FCN network layer algorithm flow chart"

Fig.4

Simulated SAR experimental data"

Fig.5

Comparison of road recognition results of various algorithms"

Table 1

Comparison on different method for road extraction from SAR image"

评价指标SVMFCNFCN-ReLUM-FCN
IoU41.760.567.669.4
ACC83.595.196.096.2

Fig.6

Optical road image labels and simulated SAR images"

Fig.7

SNR changes correspond to changes in accuracy of extracting SAR road features by M-FCN"

Table 2

Influence of SNR changes on accuracy of M-FCN in extracting road features"

SNRmIoUACC
0.259.095.4
0.362.195.7
0.463.795.7
0.563.995.8
0.664.795.9
0.765.295.8
0.866.296.0
0.969.496.3

Fig.8

Comparison of road recognition results of various algorithms from SAR images"

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