Paper Info
Title
Large-Scale Planar Block Adjustment of GaoFen1 WFV Images Covering Most of Mainland China
Abstract
GaoFen1 is the first high-resolution earth observation satellite built in China, and carries four wide field-of-view (WFV) cameras to achieve large-scale monitoring and mapping. However, the unstable attitude measurement accuracy of the satellite generally imparts low geopositioning accuracy and inconsistent geometric error in overlapping areas of WFV images. A feasible and effective large-scale planar block adjustment (PBA) method is presented that corrects the geometric errors of the vast WFV images integrally, further improving the geometric accuracy of these images. In addition, whether ground control points (GCPs) are needed, and the effect of different numbers of GCPs on PBA accuracy is also investigated. Two key technologies are used in this paper. First, a universal PBA error equation based on the virtual control points is presented to allow PBA with or without GCPs. Second, an adjustment method aided by a digital elevation model is adopted to overcome the weak convergence geometry among WFV images, further ensuring stable estimation of PBA. The effectiveness of the proposed method was verified by 664 WFV images covering most of mainland China. The satisfactory experimental results indicate that the method presented herein is reasonable and effective, but that a certain number of GCPs is needed to ensure the accuracy of large-scale PBA results for WFV images.
Year
DOI
Venue
2019
10.1109/TGRS.2018.2866286
IEEE Transactions on Geoscience and Remote Sensing
Keywords
Field
DocType
Mathematical model,Satellites,Convergence,Geometry,Optical distortion,Estimation,Orbits
Convergence (routing),Computer vision,Satellite,Virtual control,Digital elevation model,Planar,Artificial intelligence,Accuracy and precision,Earth observation satellite,Mathematics,Error equation
Journal
Volume
Issue
ISSN
57
3
0196-2892
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Yingdong Pi100.68
Bo Yang201.35
Xin Li373.72
mi48830.02
Yu-Feng Cheng502.03