Conference Proceeding

Accuracy Analysis of Generalized Imaging Models for QuickBird Stereo Imagery

Dept. of Surveying & Geo-Inf., Tongji Univ., Shanghai
01/2009; DOI:10.1109/ETTandGRS.2008.140 pp.492 - 495 In proceeding of: Education Technology and Training, 2008. and 2008 International Workshop on Geoscience and Remote Sensing. ETT and GRS 2008. International Workshop on, Volume: 2
Source: IEEE Xplore

ABSTRACT This paper analyzes the accuracies of different generalized imaging models (but RFM) for a separate-orbit QuickBird stereo imager pair, including extended DLT model, parallel projection model, projection-modified affine model, parallel perspective model, time-variant-parameterized affine model and the proposed time-variant-parameterized parallel perspective model. The experimental results have revealed the spatial dynamic characteristic of the QuickBird sensor. Due to the continuous re-orientation of the sensor, imaging models with constant parameters such as extended DLT model and affine projection model, can not describe the imaging geometry of the QuickBird imagery sufficiently. By contrast, imaging models with time-variant parameters perform much better. Meanwhile, the parallel perspective model with time-variant parameters shows superiority to the time-variant-parameterized affine model. With no less than 14 ground control points, parallel perspective model with time-variant parameters can provide the accuracy of sub-pixel level, which is close to that achieved by the more rigorous RFM.

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Keywords

14 ground control points
 
affine projection model
 
constant parameters
 
continuous re-orientation
 
different generalized imaging models
 
DLT model
 
experimental results
 
extended DLT model
 
imaging geometry
 
imaging models
 
paper analyzes
 
parallel perspective model
 
parallel projection model
 
projection-modified affine model
 
proposed time-variant-parameterized parallel perspective model
 
QuickBird imagery
 
separate-orbit QuickBird stereo imager pair
 
spatial dynamic characteristic
 
time-variant parameters
 
time-variant-parameterized affine model
 

Shijie Liu