mauricio gutierrez norbert linke quantum error detection

Owing to the volume feature of point could data, existed gross error elimination methods need spend massive memory both in space and time. Key steps of point cloud data's pro-processing focus on gross error elimination and quality control. Point cloud data has been one type of widely used data sources in the field of remote sensing. Results show that this new method can correct the phase unwrapping errors successfully in the practical application.Ī Gross Error Elimination Method for Point Cloud Data Based on Kd-Tree At last the real SAR data is tested for the phase unwrapping error correction. Results show that both methods can effectively suppress the unwrapping error when the ratio of the unwrapping errors is low, and the two methods can complement each other when the ratio of the unwrapping errors is relatively high. Then this method is compared with the L1-norm method with simulated data. The basic principle and processing steps are presented. This method identifies and corrects the unwrapping errors by establishing a functional model between the true errors and interferograms. Based on a gross error correction method, Quasi-accurate detection (QUAD), the method for unwrapping errors automatic correction is established in this paper. Unwrapping error is a common error in the InSAR processing, which will seriously degrade the accuracy of the monitoring results. Insar Unwrapping Error Correction Based on Quasi-Accurate Detection of Gross Errors (quad)

Mauricio gutierrez norbert linke quantum error detection