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Remote Sensing Technology and Application
    
Research Progress of Synthetic Aperture Radar Image Segmentation
Wan Ling1,2,3,You Hongjian1,2,3,Cheng Yuebing4,Lu Xiaojun5
(1.University of Chinese Academy of Sciences,Beijing 100039,China)
(2.Institute of Electronics,Chinese Academy of Sciences,Beijing 100190,China;
(3.Key Laboratory of Technology in Geo-spatial Information Processing and ApplicationSystem Beijing 100190,China;
4.Shanghai Eletro-Mechanical Engineering Institute,Shanghai 201109,China;
5.China International Engineering Consulting Corporation,Beijing,100048,China)
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Abstract  
Image segmentation is an indispensable part for synthetic aperture radar (SAR) image automatic interpretation.Segmentation results with high-quality are the guarantee of SAR image applications.In addition,owing to the development of SAR sensors,the segmentation task based on SAR image has been widely concerned in recent years.However,compared with the optical images,the unique properties of SAR images lead to great challenge in SAR image segmentation.With the development of pattern recognition,machine learning,remote sensing technology and other related techniques,SAR image segmentation has made great progress.This paper reviews the progress of SAR image segmentation,and then puts its emphasis on the summary of the widely used algorithms:FCM,MRF,statistical model,region information,level set,multi-scale and deep learning,etc.Finally,several viewpoints for the future research of SAR image segmentation are proposed.
Key words:  Image segmentation      Synthetic aperture radar (SAR)      FCM      MRF      Level set      Statistical model     
Received:  02 March 2017      Published:  16 March 2018
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Wan Ling
You Hongjian
Cheng Yuebing
Lu Xiaojun

Cite this article: 

Wan Ling,You Hongjian,Cheng Yuebing,Lu Xiaojun. Research Progress of Synthetic Aperture Radar Image Segmentation. Remote Sensing Technology and Application, 2018, 33(1): 10-24.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2018.1.0010     OR     http://www.rsta.ac.cn/EN/Y2018/V33/I1/10

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