Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (3): 156-161.doi: 10.11896/jsjkx.190100124

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Road Extraction Algorithm of Multi-feature High-resolution SAR Image Based on Multi-Path RefineNet

CHEN Li-fu1,LIU Yan-zhi1,ZHANG Peng1,YUAN Zhi-hui1,XING Xue-min2   

  1. (College of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China)1;
    (College of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China)2
  • Received:2019-01-16 Online:2020-03-15 Published:2020-03-30
  • About author:CHEN Li-fu,born in 1979,Ph.D,postgraduate supervisor.His main research interests include remote sensing image interpretation and deep learning. LIU Yan-zhi,born in 1992,postgradua-te.Her main research interests include image processing and deep learning.
  • Supported by:
    This work was supported by the Young Scientists Fund of the National Natural Science Foundation of China (61701047, 41701536), Excellent Youth Project of Hunan Provincial Department of Education (16B004) and Research and Innovation Project for Graduate Students in Hunan Province (CX2017B479).

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Abstract: In order to solve the problems of existing SAR image road extraction algorithm with poor automation and poor universality,a multi-feature road extraction algorithm was proposed based on the multi-path refinement network.Firstly,gabor transformation and gray level-gradient co-occurrence matrix transformation are performed on SAR images to obtain rich road feature information.A multi-path refinement network is formed by coupling the cascade refinement network and the residual network.Then,the SAR original image,the acquired low-level feature image and the label image are input into the new network for trai-ning,and the road features extracted from each layer of network are fully utilized to obtain the initial road segmentation results.Finally,mathematical morphology operation is used to connect the initial road fracture and remove false alarm.This algorithm is used for road extraction of SAR images with different resolutions.The experimental results show that this algorithm has a wide range of application in SAR image extraction and the effect of road extraction is better.

Key words: Deep learning, Feature extraction, Mathematical morphology operation, Road extraction, Synthetic aperture radar (SAR)

CLC Number: 

  • TP753
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