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

Refined Equivalent Pinhole Model for Large-scale 3D Reconstruction from  Spaceborne CCD Imagery

A great deal of research has demonstrated recently that multi-view stereo
(MVS) matching can be solved with deep learning methods. However, these efforts
were focused on close-range objects and only a very few of the deep
learning-based methods were specifically designed for large-scale 3D urban
reconstruction due to the lack of multi-view aerial image benchmarks. In this
paper, we present a synthetic aerial dataset, called the WHU dataset, we
created for MVS tasks, which, to our knowledge, is the first large-scale
multi-view aerial dataset. It was generated from a highly accurate 3D digital
surface model produced from thousands of real aerial images with precise camera
parameters. We also introduce in this paper a novel network, called RED-Net,
for wide-range depth inference, which we developed from a recurrent
encoder-decoder structure to regularize cost maps across depths and a 2D fully
convolutional network as framework. RED-Net's low memory requirements and high
performance make it suitable for large-scale and highly accurate 3D Earth
surface reconstruction. Our experiments confirmed that not only did our method
exceed the current state-of-the-art MVS methods by more than 50% mean absolute
error (MAE) with less memory and computational cost, but its efficiency as
well. It outperformed one of the best commercial software programs based on
conventional methods, improving their efficiency 16 times over. Moreover, we
proved that our RED-Net model pre-trained on the synthetic WHU dataset can be
efficiently transferred to very different multi-view aerial image datasets
without any fine-tuning. Dataset are available at this http URL

本研究提出了一个名为 WHU 的合成航空数据集，该数据集是首个大规模的多视角航空数据集，并介绍了一种名为 RED-Net 的新型神经网络，专门用于高精度的三维地球表面重建。通过实验，我们证实了该方法的性能和效率，在减少内存和计算成本的同时，使 MAE 提高了 50％以上，并且比传统方法的一些最佳商业软件的效率提高了 16 倍。