Optimization Method for DSM Reconstruction of Equivalent Pinhole Model Based on Iterative Resampling and Chunking Process

The development of satellite remote sensing technology has made it easier to obtain satellite imagery. Compared to imagery obtained from aerial photography, satellite imagery has the advantages of wide coverage, high acquisition efficiency, and periodic revisits. In photogrammetry, the 3-D reconstruction technology of satellite imagery often requires optimization and adjustment of numerous rational polynomial coefficient parameters, which to some extent limits the speed and accuracy of 3-D reconstruction. At the same time, the progress in 3-D reconstruction technology in the field of computer vision has shown certain advantages in terms of accuracy and speed. However, these methods are specifically designed for pinhole imaging models and cannot be directly applied to the 3-D reconstruction of satellite imagery with row-sampled central projection. The introduction of the equivalent pinhole imaging model enables computer vision methods to perform 3-D reconstruction on satellite imagery. This local approximation introduces reprojection errors when the RPC model is equivalent to the pinhole imaging model, thereby affecting the accuracy of 3-D reconstruction. This article investigates the causes and patterns of reprojection errors in the equivalent pinhole imaging model and proposes a method for generating pseudoimages through iterative resampling, as well as a method for partitioning satellite images to equivalently approximate the pinhole imaging model. Test results on the MVS3D dataset show that both methods can reduce reprojection errors, thereby improving the accuracy of 3-D reconstruction of satellite images using the equivalent pinhole imaging model.