Complex Least Squares Adjustment and Its Application in Tree Height Inversion with POLInSAR Data

Abstract

Abstract:

The surveying observations in traditional geodesy and remote sensing usually are real numbers, so the theory of surveying adjustment is based on real space. However, in modern geodesy and remote sensing, more and more observations are expressed in complex form. As same as the real number observations, the complex number observations are also facing the problem that how to identify the best estimations of unknown parameters from the observations with errors. However, data processing methods involving complex observations are mainly step-by-step or direct solver based on the observation process which cannot consider observation errors, redundant observation and so on. For this situation, this paper introduces least squares methods of complex data processing and tries to extend surveying adjustments from the real number space to the complex number space. Meanwhile, the two adjustment criteria in complex domain are compared quantitatively. In order to understand effectiveness of complex least squares, the tree height inversion from POLInSAR data is taken as an example. We firstly establish complex adjustment function model and stochastic model for POLInSAR tree height inversion and apply complex least squares method to estimate tree height. The results show that the complex least squares approach is reliable and better than other classic tree height retrieval methods. Besides, the method is simple and easy to realize.

Key words: Surveying adjustment, Complex least squares, Polarimetric interferometric SAR (POLInSAR), Tree height inversion, Three-stage algorithm

CLC Number:

P207

References

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