A Classification Method Based on Polarimetric Entropy and GEV Mixture Model for Intertidal Area of PolSAR Image

She Xiaoqiang; Qiu Xiaolan; Lei Bin; Zhang Wei; Lu Xiaojun

doi:10.12000/JR16149

Volume 6 Issue 5

Oct. 2017

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Article Navigation > Journal of Radars > 2017 > 6(5): 554-563

She Xiaoqiang, Qiu Xiaolan, Lei Bin, Zhang Wei, Lu Xiaojun. A Classification Method Based on Polarimetric Entropy and GEV Mixture Model for Intertidal Area of PolSAR Image[J]. Journal of Radars, 2017, 6(5): 554-563. doi: 10.12000/JR16149

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She Xiaoqiang, Qiu Xiaolan, Lei Bin, Zhang Wei, Lu Xiaojun. A Classification Method Based on Polarimetric Entropy and GEV Mixture Model for Intertidal Area of PolSAR Image[J]. Journal of Radars, 2017, 6(5): 554-563. doi: 10.12000/JR16149

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A Classification Method Based on Polarimetric Entropy and GEV Mixture Model for Intertidal Area of PolSAR Image

DOI: 10.12000/JR16149

She Xiaoqiang^{1,2,3
,
,},
Qiu Xiaolan^{1,2
,},
Lei Bin^{1,2
,},
Zhang Wei^4
,,
Lu Xiaojun^5
,

①.
Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
②.
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
③.
University of Chinese Academy of Sciences, Beijing 100190, China
④.
National Disaster Reduction Center of the Ministry of Civil Affairs, Beijing 100124, China
⑤.
China International Engineering Consulting Corporation, Beijing 100048, China

Funds: The National Natural Science Foundation of China (61331017), The Key Standard Technologies of National High Resolution Special (30-Y20A12-9004-15/16)

Received Date: 2016-12-20
Rev Recd Date: 2017-02-17

Available Online: 2017-04-18

Publish Date: 2017-10-28

Abstract

Abstract

This paper proposes a classification method for the intertidal area using quad-polarimetric synthetic aperture radar data. In this paper, a systematic comparison of four well-known multipolarization features is provided so that appropriate features can be selected based on the characteristics of the intertidal area. Analysis result shows that the two most powerful multipolarization features are polarimetric entropy and anisotropy. Furthermore, through our detailed analysis of the scattering mechanisms of the polarimetric entropy, the Generalized Extreme Value (GEV) distribution is employed to describe the statistical characteristics of the intertidal area based on the extreme value theory. Consequently, a new classification method is proposed by combining the GEV Mixture Models and the EM algorithm. Finally, experiments are performed on the Radarsat-2 quad-polarization data of the Dongtan intertidal area, Shanghai, to validate our method.

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References

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