Modified version of three-component model-based decomposition for polarimetric SAR data

doi:10.21629/JSEE.2019.02.06

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (2): 270-277.doi: 10.21629/JSEE.2019.02.06

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Modified version of three-component model-based decomposition for polarimetric SAR data

Shuang ZHANG^1,*(), Xiangchuan YU²(), Lu WANG¹()

¹ School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China
² Caltta Technologies Co., Ltd., Beijing 100191, China

Received:2018-05-16 Online:2019-04-01 Published:2019-04-28
Contact: Shuang ZHANG E-mail:zhangshuang@xaut.edu.cn;yu.xiangchuan@zte.com.cn;xidain@163.com
About author:ZHANG Shuang was born in 1983. She received her B.S. degree in School of Information Engineering from Liaoning University in 2006, Shenyang, China. She received her Ph.D. degree in School of Electronic Engineering from Xidian University, Xi'an, China, in 2015. She is currently a lecturer at School of Automation and Information Engineering, Xi'an University of Technology, Xi'an, China. Her research interests are interpretation and processing of PolSAR data and deep learning in the complicated data. E-mail:zhangshuang@xaut.edu.cn|YU Xiangchuan was born in 1984. He received his B. S. and M.S. degrees in School of Electronic Engineering from Xi'an University in 2006 and 2009 respectively, Xi'an, China. Now, he is an engineer in Caltta Technologies Co., Ltd. His main research interests are private network communication and image processing. E-mail:yu.xiangchuan@zte.com.cn|WANG Lu was born in 1986. She received her B.S. degree in School of Electronic Engineering from Xi'an University of Posts and Telecommunications, Xi'an, China, in 2008, and her Ph.D. degree in School of Electronic Engineering from Xidian University, Xi'an, China, in 2014. She is currently a lecture at School of Automation and Information Engineering, Xi'an University of Technology, Xi'an, China. Her research interests are X-ray pulsar navigation and weak radar signal processing. E-mail:wanglu xidain@163.com
Supported by:
the National Natural Science Foundation of China(41704118);the National Natural Science Foundation of China(11747032);the Natural Science Basic Research Plan in Shaanxi Province of China(2017JQ6065);the Natural Science Basic Research Plan in Shaanxi Province of China(2017JQ4017);the Special Scientific Research Project of Shaanxi Provincial Education Department(18JK0549);This work was supported by the National Natural Science Foundation of China (41704118; 11747032), the Natural Science Basic Research Plan in Shaanxi Province of China (2017JQ6065; 2017JQ4017), and the Special Scientific Research Project of Shaanxi Provincial Education Department (18JK0549)

Abstract

Abstract:

A new hybrid Freeman/eigenvalue decomposition based on the orientation angle compensation and the various extended volume models for polarimetric synthetic aperture radar (PolSAR) data are presented. There are three steps in the novel version of the three-component model-based decomposition. Firstly, two special unitary transform matrices are applied on the coherency matrix for deorientation to decrease the correlation between the co-polarized term and the cross-polarized term. Secondly, two new conditions are proposed to distinguish the manmade structures and the nature media after the orientation angle compensation. Finally, in order to adapt to the scattering properties of different media, five different volume scattering models are used to decompose the coherency matrix. These new conditions pre-resolves man-made structures, which is beneficial to the subsequent selection of a more suitable volume scattering model. Fully PolSAR data on San Francisco are used in the experiments to prove the efficiency of the proposed hybrid Freeman/eigenvalue decomposition.

Key words: polarimetric synthetic aperture radar (PolSAR), radar polarimetry hybrid Freeman/eigenvalue decomposition, scattering model

Shuang ZHANG, Xiangchuan YU, Lu WANG. Modified version of three-component model-based decomposition for polarimetric SAR data[J]. Journal of Systems Engineering and Electronics, 2019, 30(2): 270-277.

Figures/Tables 9

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References 40

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Diagonal terms	Physical meaning
$T_{11}$	Target symmetry
$T_{22}$	Target non-symmetry
$T_{33}$	Target irregularity

Sensor	Band	Looks	Resolution/m	Incidence angle/(°)
AIRSAR	L	4	$10\times 10$	5-60

Zone	Size/pixels	C1/pixels	C2/pixels	Percentage/%
Zone 1	4 000	—	4 000	100
Zone 2	3 000	2 348	—	78.48
Zone 3	3 000	1	104	3.5

Method	${\rm mean}\_p_{s}$	${\rm mean}\_p_d$	${\rm mean}\_p_v$
FDD1	0.904	0	0.095
FDD2	0.906	0	0.093
HFED1	0.872	0.043	0.085
HFED2	0.872	0.043	0.085
PD	0.929	0	0.070

Method	${\rm mean}\_p_{s}$	${\rm mean}\_p_{d}$	${\rm mean}\_p_v$
FDD1	0.059	0.367	0.579
FDD2	0.138	0.488	0.375
HFED1	0.140	0.485	0.375
HFED2	0.312	0.497	0.186
PD	0.037	0.765	0.220

Modified version of three-component model-based decomposition for polarimetric SAR data

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[1]	Bin Zhang, Guorui Ma2, Zhi Zhang, and Qianqing Qin. Region-based classification by combining MS segmentation and MRF for POLSAR images [J]. Journal of Systems Engineering and Electronics, 2013, 24(3): 400-.
[2]	Bo Pang, Shiqi Xing, Yongzhen Li, and Xuesong Wang. Novel polarimetric SAR speckle filtering algorithm based on mean shift [J]. Journal of Systems Engineering and Electronics, 2013, 24(2): 222-233.

Method	${\rm mean}\_p_{s}$	${\rm mean}\_p_{d}$	${\rm mean}\_p_{v}$
FDD1	0.072	0.175	0.763
FDD2	0.025	0.163	0.815
HFED1	0.087	0.093	0.819
HFED2	0.109	0.093	0.796
PD	0.115	0.023	0.866