Title
The In-Orbit Calibration Method Based on Terrain Matching With Pyramid-Search for the Spaceborne Laser Altimeter
Abstract
The pointing bias of the laser altimeter would change because of the launch vibration, variations in the space environment or other factors, which is one of the most important factors affecting the geometric accuracy of a laser altimeter. To calibrate pointing bias and improve the measuring accuracy of spaceborne laser altimeter, this paper proposed an in-orbit calibration method based on terrain matching with pyramid-search for this spaceborne laser altimeter. First, we used published digital terrain data as a reference to match with stripes of a point cloud obtained by the geolocation model of the laser altimeter, and then the optimal matching terrain could be determined by a pyramid search. Finally, the pointing bias of the laser altimeter was calibrated. The proposed method was applied in calibration experiment of ZY3-02 laser altimeter. Several tracks of ZY3-02 laser data and advanced Land Observation Satellite Digital Surface Model (AW3D30) with a 30-m grid size were deployed for calibration and validation. After calibration, the systematic error was effectively eliminated. The elevation accuracy of the laser altimeter was improved from more than 100 to 3 m approximately, and the algorithm efficiency with pyramid search was improved by 10 times at least. The experimental result demonstrates that the proposed method is an effective means to calibrate the current spaceborne laser altimeters.
Year
DOI
Venue
2019
10.1109/JSTARS.2018.2890552
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Keywords
Field
DocType
Laser modes,Satellites,Calibration,Measurement by laser beam,Laser theory,Laser transitions
Altimeter,Satellite,Terrain,Remote sensing,Laser,Pyramid,Elevation,Point cloud,Mathematics,Calibration
Journal
Volume
Issue
ISSN
12
3
1939-1404
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Xinming Tang1265.52
Junfeng Xie2268.40
Xiaoming Gao3135.08
Fan Mo4627.64
Wanwan Feng500.34
Ren Liu6286.14