Evaluation demonstrates reduced Root Mean Square Errors in rectified images through advanced scanline methods, indicating enhanced processing efficiency.
The extraction of accurate information from high-resolution satellite images hinges upon the availability of robust mathematical models for sensor modeling. A key aspect involves the adjustment of the Exterior Orientation Parameters (EOPs) and Interior Orientation Parameters (IOPs) of the sensor to optimize accuracy. However, most satellite providers opt not to disclose their sensor models. This paper introduces a rigorous mathematical model based on Multiple Projection Center (MPC) for inaccurately geo-referenced rectified images, where the original image geometry is distorted. The proposed method involves converting the geo-referenced images back to their original geometry at the time of imaging, applying MPC, and evaluates various Best Scanline Search/Determination (BSS/BSD) techniques. The proposed method was applied to five groups of distinct images, achieving the Root Mean Square Errors (RMSEs) from 0.24 to 0.28 (pixels). The method outperformed previous studies and reduced computational time by over 98%, making it suitable for near-real-time photogrammetric tasks.
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Nezhad et al. (2025) studied this question.
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