11 September 2015 Evaluation of positioning error-induced pixel shifts on satellite linear push-broom imagery
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J. of Applied Remote Sensing, 9(1), 095061 (2015). doi:10.1117/1.JRS.9.095061
Georeferencing is one of the major tasks of satellite-borne remote sensing. Compared to traditional indirect methods, direct georeferencing through a Global Positioning System/inertial navigation system requires fewer and simpler steps to obtain exterior orientation parameters of remotely sensed images. However, the pixel shift caused by geographic positioning error, which is generally derived from boresight angle as well as terrain topography variation, can have a great impact on the precision of georeferencing. The distribution of pixel shifts introduced by the positioning error on a satellite linear push-broom image is quantitatively analyzed. We use the variation of the object space coordinate to simulate different kinds of positioning errors and terrain topography. Then a total differential method was applied to establish a rigorous sensor model in order to mathematically obtain the relationship between pixel shift and positioning error. Finally, two simulation experiments are conducted using the imaging parameters of Chang’ E-1 satellite to evaluate two different kinds of positioning errors. The experimental results have shown that with the experimental parameters, the maximum pixel shift could reach 1.74 pixels. The proposed approach can be extended to a generic application for imaging error modeling in remote sensing with terrain variation.
Wang, Li, Wei, Jia, and Liu: Evaluation of positioning error-induced pixel shifts on satellite linear push-broom imagery
© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Xiangjun Wang, Yang Li, Hong Wei, Guimin Jia, Feng Liu, "Evaluation of positioning error-induced pixel shifts on satellite linear push-broom imagery," Journal of Applied Remote Sensing 9(1), 095061 (11 September 2015). https://doi.org/10.1117/1.JRS.9.095061


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