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Building Height Retrieval from Dual\|aspect SAR Images based on Match of Strong Backscattering Features |
Xu Xu1,2,Zhang Fengli1,Wang Guojun1,Fu Xiyou1,2,Sha Minmin1,2,Li Zhikun1,2,Shao Yun1,Chen Longyong3,Liang Xingdong3
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(1.Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China;
2.University of Chinese Academy of Sciences,Beijing 100049,China;
3.Institute of Electronics,Chinese Academy of Sciences,Beijing 100190,China) |
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Abstract Building height retrieval from single SAR image is quite difficult due to complexity of urban scene and geometric distortion of SAR imaging system.Aiming at this problem,a new method for building height retrieval was proposed by matching between geometric model and strong backscattering features in dual\|aspect SAR images,because strong backscattering features received by SAR sensor formed by layover,double bounce scattering,and strong odd scattering is very distinct and sensitive to orientation.Scattering characteristics of building in SAR image,as well as its sensitivity to SAR imaging orientation was first analyzed.Then geometric model of strong backscattering features for building in dual\|aspect SAR images was constructed.And then matching function was defined based on backscattering mean,probability density function of backscattering,and boundary information.Finally building height was derived using multi\|population genetic algorithm to optimize matching function.The experiments based on simulated and airborne dual\|aspect SAR images showed that the average error for building height retrieval using the proposed method is smaller than 1 meter,thus it can effectively improve the building height retrieval results from SAR data.
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Received: 26 August 2014
Published: 05 April 2016
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Cite this article:
Xu Xu,Zhang Fengli,Wang Guojun,Fu Xiyou,Sha Minmin,Li Zhikun,Shao Yun,Chen Longyong,Liang Xingdong. Building Height Retrieval from Dual\|aspect SAR Images based on Match of Strong Backscattering Features. Remote Sensing Technology and Application, 2016, 31(1): 149-156.
URL:
http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2016.1.0149 OR http://www.rsta.ac.cn/EN/Y2016/V31/I1/149
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