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In this study, we present a large-scale earth surface reconstruction pipelinefor linear-array charge-coupled device (CCD) satellite imagery. Whilemainstream satellite image-based reconstruction approaches performexceptionally well, the rational functional model (RFM) is subject to severallimitations. For example, the RFM has no rigorous physical interpretation anddiffers significantly from the pinhole imaging model; hence, it cannot bedirectly applied to learning-based 3D reconstruction networks and to more novelreconstruction pipelines in computer vision. Hence, in this study, we introducea method in which the RFM is equivalent to the pinhole camera model (PCM),meaning that the internal and external parameters of the pinhole camera areused instead of the rational polynomial coefficient parameters. We then derivean error formula for this equivalent pinhole model for the first time,demonstrating the influence of the image size on the accuracy of thereconstruction. In addition, we propose a polynomial image refinement modelthat minimizes equivalent errors via the least squares method. The experimentswere conducted using four image datasets: WHU-TLC, DFC2019, ISPRS-ZY3, and GF7.The results demonstrated that the reconstruction accuracy was proportional tothe image size. Our polynomial image refinement model significantly enhancedthe accuracy and completeness of the reconstruction, and achieved moresignificant improvements for larger-scale images.

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