| Autor: |
Qing Fu, Xiaohua Tong, Shijie Liu, Zhen Ye, Yanmin Jin, Hanyu Wang, Zhonghua Hong |
| Předmět: |
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| Zdroj: |
Photogrammetric Engineering & Remote Sensing; Apr2023, Vol. 89 Issue 4, p211-220, 10p |
| Abstrakt: |
The precise geo-positioning of high-resolution satellite imagery (HRSI) without ground control points (GCPs) is an important and fundamental step in global mapping, three-dimensional modeling, and so on. In this paper, to improve the efficiency of large-scale bundle adjustment (BA), we propose a combined Preconditioned Conjugate Gradient (PCG) and Graphic Processing Unit (GPU) parallel computing approach for the BA of large-scale HRSI without GCPs. The proposed approach consists of three main components: 1) construction of a BA model without GCPs; 2) reduction of memory consumption using the Compressed Sparse Row sparse matrix format; and 3) improvement of the computational efficiency by the use of the combined PCG and GPU parallel computing method. The experimental results showed that the proposed method: 1) consumes less memory consumption compared to the conventional full matrix format method; 2) demonstrates higher computational efficiency than the single-core, Ceressolver and multi-core central processing unit computing methods, with 9.48, 6.82, and 3.05 times faster than the above three methods, respectively; 3) obtains comparable BA accuracy with the above three methods, with image residuals of about 0.9 pixels; and 4) is superior to the parallel bundle adjustment method in the reprojection error [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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