A point cloud optimization algorithm based on nonadjacent low-altitude remote sensing images

Nan Yang; Lei Huang; Haonan Chang

doi:10.1117/12.3004022

11 October 2023 A point cloud optimization algorithm based on nonadjacent low-altitude remote sensing images

Nan Yang, Lei Huang, Haonan Chang

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128001A (2023) https://doi.org/10.1117/12.3004022
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

This paper presents a robust point cloud optimization algorithm based on nonadjacent low-altitude remote sensing images. The proposed algorithm is designed to optimize the performance of point cloud generation in accuracy and efficiency. In order to accelerate the process of stereo matching and compute the coordinate of object point accurately, a pair of nonadjacent images is employed to lengthen the epipolar line of the image pair. Then a patch based Least Square Matching (LSM) method is utilized to search the optimal matching pixels and compute the coordinates of corresponding object points in 3D space. Comparison studies and experimental results in point cloud generation about low altitude remote sensing images have proved the effectiveness of the proposed algorithm.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nan Yang, Lei Huang, and Haonan Chang "A point cloud optimization algorithm based on nonadjacent low-altitude remote sensing images", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128001A (11 October 2023); https://doi.org/10.1117/12.3004022

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