| Summary: | 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.
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