Refined equivalent pinhole model for large-scale 3D reconstruction from spaceborne CCD imagery

Automatic 3D reconstruction from spaceborne charge-coupled device (CCD) optical imagery is still a challenge as the rational functional model (RFM) based reconstruction pipeline failed to amount to the advances of pinhole based approaches in computer vision and photogrammetry. As a consequence, the accuracy and completeness of the reconstructed surface by RFM based pipeline improved slightly recent years. Though the perspective camera approximation model was explored to convert the RFM to pinhole model, it could hardly guarantee the reconstruction accuracy due to the re-projection error introduced when approximating the linear push broom camera to perspective camera. Hence, we present a refined equivalent pinhole model (REPM) for 3D reconstruction from spaceborne CCD imagery. We initially investigated the aspects that influence the re-projection error thru mathematical induction and discovered that the image size and height range of the captured area are the two key factors. To ensure the performance of the 3D reconstruction while minimizing the re-projection error, we explored the optimal image size to crop large-scale image with, while alleviated the height range effect on the image space by re-projecting the cropped images to be closed to the pseudo-image that is captured by the approximated perspective camera. The above-mentioned improvements are implemented in an image partition module and an image geometric correction module respectively, and are encompassed in the proposed REPM-based 3D reconstruction pipeline. We conducted extensive experiments on different images covering various areas from different linear-array CCD sensors to verify the proposed approach. The results indicate that our pipeline can achieve higher accuracy and completeness and exhibits great potential. The implementation of the pipeline is available at here . • A refined equivalent pinhole model is proposed for 3D reconstruction of CCD imagery. • REPM innovatively combines image partition and image geometric correction. • REPM exhibits superior potential compared with other solutions.