Complex Least Squares Adjustment to Improve Tree Height Inversion Problem in PolInSAR

doi:10.11947/j.JGGS.2019.0101

Journal of Geodesy and Geoinformation Science ›› 2019, Vol. 2 ›› Issue (1): 1-8.doi: 10.11947/j.JGGS.2019.0101

Complex Least Squares Adjustment to Improve Tree Height Inversion Problem in PolInSAR

Jianjun ZHU,Qinghua XIE(),Tingying ZUO,Changcheng WANG,Jian XIE

School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

Received:2018-04-27 Accepted:2019-01-11 Online:2019-03-20 Published:2020-03-20
Contact: Qinghua XIE E-mail:csuxqh@csu.edu.cn
About author:Jianjun ZHU(1962—), male, professor, PhD supervisor, majors in surveying adjustment and data processing.E-mail: zjj@csu.edu.cn
Supported by:
The National Natural Science Foundation of China(41274010);The National Natural Science Foundation of China(40974007);The National Natural Science Foundation of China(40901172);National Key Basic Research and Development Program of China(2012AA121301);Hunan Provincial Natural Science Foundation of China(12JJ4035);Postgraduate Autonomous Exploration Project of Central South University(2013zzts055);China Scholarship Council(201406370079)

Abstract

Abstract:

At present, the principal data processing methods involving complex observations are based on two strategies according to characteristics of the observation process, i.e., step-by-step and direct resolution. However, these strategies have some limitations, e.g. they cannot consider statistical observation error information, redundant observations and so on. This paper applies least squares methods to complex data processing to extend surveying adjustment theory from real to complex number space. We compared the two adjustment criteria for a complex domain in a quantitative way. In order to understand the effectiveness of complex least squares, tree height inversion from PolInSAR data is taken as an example. We firstly established both a complex adjustment function model and a stochastic model for PolInSAR tree height inversion, and then applied the complex least squares method to estimate tree height. Results show that the complex least squares approach is reliable and outperforms other classic tree height retrieval methods; the method is simple and easy to implement.

Key words: surveying adjustment; complex least squares; polarimetric interferometric SAR (PolInSAR); tree height inversion; three-stage algorithm

Jianjun ZHU,Qinghua XIE,Tingying ZUO,Changcheng WANG,Jian XIE. Complex Least Squares Adjustment to Improve Tree Height Inversion Problem in PolInSAR[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(1): 1-8.

Figures/Tables 7

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References 23

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Criterion	Solution method
(4)	Direct	5.92	10.97
(4)	Iterative	5.92	10.97
(5)	Direct	0.04	0.25
(5)	Iterative	0.04	0.25

Inversion methods	Tree height truth/m	Mean tree height /m	Mean error /m	RMSE /m	Number of samples
DEM differencing	10	4.18	5.82	5.88	10000
Three-stage		8.92	1.15	1.34
Complex least squares		9.17	0.92	1.08

Complex Least Squares Adjustment to Improve Tree Height Inversion Problem in PolInSAR

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