A fast non-iterative method for the object to image space best scanline determination of spaceborne linear array pushbroom images

Abstract

The back-projection of three-dimensional (3D) object coordinates append onto the two-dimensional (2D) image space is the principal process of several photogrammetric tasks. Unlike frame-type images, each scanline of linear array images has six exterior orientation parameters (EOPs) at the exposure. Consequently, it is not possible to directly convert 3D object coordinates to 2D image coordinates by the Collinearity Equation (CE) unless precise EOPs have been determined for each scanline. Therefore, determining the best scanline is a pre-requisite step for the object-to-image transformation. Previous best scanline determination (BSD) methods utilized iterative procedures, becoming time-consuming and inefficient for near-real-time applications. This paper introduces a novel non-iterative three-stage methodology for the BSD of spaceborne linear pushbroom images, recording the Earth’s surface information. First, the approximate times of exposure of simulated control points (SCOPs) were computed. Afterwards, two separate approaches: (1) artificial neural networks (ANN) and (2) optimized global polynomial (OGP) were employed to model the relationship between approximate and exact exposure times. Finally, the best scanline of each unknown point was determined by refining the approximate exposure time using one of the models adopted in the previous step, regardless of the iterative procedure. The proposed method was applied to eight different images acquired by six sensors, and eight million simulated check points (SCPs) per image were utilized for statistical assessments. The achieved root mean square errors (RMSEs) of the proposed BSD method in eight images varied between 0.20 and 0.46 (pixel), demonstrating the proposed method’s potential to obtain desirable sub-pixel accuracy. Additionally, the experimental results revealed that the proposed method outperformed other well-known algorithms such as the Newton-Raphson (NR), the Bisecting Window Search (BWS), and the Sequential Search (SS) algorithms. Both proposed approaches significantly reduced over 95% computation time, suggesting the applicability of the proposed workflow for near-real-time photogrammetric tasks.