期刊
COMPUTERS & GEOSCIENCES
卷 92, 期 -, 页码 70-78出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cageo.2016.04.006
关键词
Bundle block adjustment; Large-scale remote sensing data; Block-based Sparse Matrix Compression; Preconditioned Conjugate Gradient
资金
- China Postdocroral Science Foudation [2015M572224]
- National Natural Science Foundation of China [41322010]
- Key Laboratory for Aerial Remote Sensing Technology of National Administration of Surveying, Mapping and Geoinformation (NASG) [2014B01]
- Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [CUG160838]
In recent years, new platforms and sensors in photogrammetry, remote sensing and computer vision areas have become available, such as Unmanned Aircraft Vehicles (UAV), oblique camera systems, common digital cameras and even mobile phone cameras. Images collected by all these kinds of sensors could be used as remote sensing data sources. These sensors can obtain large-scale remote sensing data which consist of a great number of images. Bundle block adjustment of large-scale data with conventional algorithm is very time and space (memory) consuming due to the super large normal matrix arising from large-scale data. In this paper, an efficient Block-based Sparse Matrix Compression (BSMC) method combined with the Preconditioned Conjugate Gradient (PCG) algorithm is chosen to develop a stable and efficient bundle block adjustment system in order to deal with the large-scale remote sensing data. The main contribution of this work is the BSMC-based PCG algorithm which is more efficient in time and memory than the traditional algorithm without compromising the accuracy. Totally 8 datasets of real data are used to test our proposed method. Preliminary results have shown that the BSMC method can efficiently decrease the time and memory requirement of large-scale data. (C) 2016 Elsevier Ltd. All rights reserved.
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