In this study, we present a large-scale earth surface reconstruction pipeline for linear-array charge-coupled device (CCD) satellite imagery. While mainstream satellite image-based reconstruction approaches perform exceptionally well, the rational functional model (RFM) is subject to several limitations. For example, the RFM has no rigorous physical interpretation and differs significantly from the pinhole imaging model; hence, it cannot be directly applied to learning-based 3D reconstruction networks and to more novel reconstruction pipelines in computer vision. Hence, in this study, we introduce a method in which the RFM is equivalent to the pinhole camera model (PCM), meaning that the internal and external parameters of the pinhole camera are used instead of the rational polynomial coefficient parameters. We then derive an error formula for this equivalent pinhole model for the first time, demonstrating the influence of the image size on the accuracy of the reconstruction. In addition, we propose a polynomial image refinement model that minimizes equivalent errors via the least squares method. The experiments were conducted using four image datasets: WHU-TLC, DFC2019, ISPRS-ZY3, and GF7. The results demonstrated that the reconstruction accuracy was proportional to the image size. Our polynomial image refinement model significantly enhanced the accuracy and completeness of the reconstruction, and achieved more significant improvements for larger-scale images.

本研究介绍了一种大规模地球表面重建管道，适用于线性阵列电荷耦合器（CCD）卫星图像。通过引入等效针孔相机模型，我们得出了与镜头内外参数相关的误差公式，并提出了一种通过最小二乘法最小化等效误差的多项式图像细化模型。实验证明，我们的方法显著提高了重建的准确性和完整性，对于更大规模的图像获得了更明显的改进效果。

基于卫星CCD图像的大规模三维重建的改进等效针孔模型