Refined Equivalent Pinhole Model for Large-scale 3D Reconstruction from
  Spaceborne CCD Imagery
Hong Danyang Yu Anzhu Ji Song Cao Xuefeng Quan Yujun Guo Wenyue Qiu Chunping
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.