A Baghani; M.J Valadan Zoej; M Mokhtarzade
Volume 7, Issue 2 , November 2015, , Pages 1-18
Abstract
Due to the absence of either satellite ephemeris information or camera model for various high resolution satellite images, rational functions models (RFMs) are widely used by photogrammetric and remote sensing communities. This method has various disadvantages such as: The dependency of this method on ...
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Due to the absence of either satellite ephemeris information or camera model for various high resolution satellite images, rational functions models (RFMs) are widely used by photogrammetric and remote sensing communities. This method has various disadvantages such as: The dependency of this method on many ground control points (GCPs), numerical complexity and particularly terms selection. As there is no physical meaning for the terms of RFM, in traditional solution all of them are involved in the computational process which causes over-parameterization. In this letter, a modified Ant Colony Optimization is applied to identify the optimal terms for RFMs. For this purpose this method is tested on three images with different geometric correction levels, different coordinate systems (UTM, CT & Geodetic) and different combination of Ground Control Points (GCPs) and Independent Check Points (ICPs), without normalization of the image and ground coordinates. Experimental results demonstrate how well the proposed algorithm can determine an RFM, which is optimal in both the total number of terms and the positional accuracy. The results have showed that the CT coordinate system has the better capability in accuracy and convergence’s speed. As a conclusion, ACO when using for RFM optimization, can achieve subpixel accuracy even with just four GCPs.
