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.

附加到二维 (2D) 图像空间的三维 (3D) 对象坐标的反投影是几个摄影测量任务的主要过程。与帧型图像不同，线阵图像的每条扫描线在曝光时具有六个外定向参数 (EOP)。因此，除非为每条扫描线确定了精确的 EOP，否则不可能通过共线性方程 (CE) 将 3D 对象坐标直接转换为 2D 图像坐标。因此，确定最佳扫描线是对象到图像转换的先决步骤。以前的最佳扫描线确定 (BSD) 方法使用迭代程序，对于近实时应用程序变得耗时且效率低下。本文介绍了一种新的非迭代三阶段方法，用于星载线性推扫图像的 BSD，记录地球表面信息。首先，计算模拟控制点 (SCOP) 的大致曝光时间。之后，采用两种不同的方法：（1）人工神经网络（ANN）和（2）优化全局多项式（OGP）来模拟近似和精确曝光时间之间的关系。最后，通过使用上一步中采用的模型之一细化近似曝光时间来确定每个未知点的最佳扫描线，而不管迭代过程如何。所提出的方法应用于由六个传感器获取的八幅不同图像，每幅图像有八百万个模拟检查点 (SCP) 用于统计评估。所提出的 BSD 方法在八幅图像中实现的均方根误差 (RMSE) 在 0.20 和 0.46（像素）之间变化，证明了所提出的方法有可能获得理想的亚像素精度。此外，实验结果表明，所提出的方法优于其他众所周知的算法，如牛顿-拉夫森 (NR)、二等分窗口搜索 (BWS) 和顺序搜索 (SS) 算法。两种提议的方法都显着减少了 95% 以上的计算时间，表明提议的工作流程适用于近实时摄影测量任务。实验结果表明，所提出的方法优于其他众所周知的算法，如牛顿-拉夫森 (NR)、二等分窗口搜索 (BWS) 和顺序搜索 (SS) 算法。两种提议的方法都显着减少了 95% 以上的计算时间，表明提议的工作流程适用于近实时摄影测量任务。实验结果表明，所提出的方法优于其他众所周知的算法，如牛顿-拉夫森 (NR)、二等分窗口搜索 (BWS) 和顺序搜索 (SS) 算法。两种提议的方法都显着减少了 95% 以上的计算时间，表明提议的工作流程适用于近实时摄影测量任务。