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Optimal selection of virtual control points with planar constraints for large‐scale block adjustment of satellite imagery
Author(s) -
Tong Xiaohua,
Fu Qing,
Liu Shijie,
Wang Hanyu,
Ye Zhen,
Jin Yanmin,
Chen Peng,
Xu Xiong,
Wang Chao,
Liu Sicong,
Hong Zhonghua,
Luan Kuifeng
Publication year - 2020
Publication title -
the photogrammetric record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 51
eISSN - 1477-9730
pISSN - 0031-868X
DOI - 10.1111/phor.12339
Subject(s) - block (permutation group theory) , computer science , satellite , intersection (aeronautics) , scale (ratio) , selection (genetic algorithm) , satellite imagery , grid , elevation (ballistics) , mathematical optimization , artificial intelligence , algorithm , mathematics , remote sensing , geodesy , geography , engineering , cartography , geometry , aerospace engineering
In large‐scale block adjustment of high‐resolution satellite imagery (HRSI) without ground control points (GCPs), the selection of virtual control points (VCPs) is an important factor in determining the block adjustment accuracy. The traditional VCP generation method is based on a regular grid and uses the initial rational polynomial coefficient (RPC) files but without considering topographic factors. To further improve the accuracy, this paper proposes an approach for the optimal selection of VCPs with planar constraints, which ensures that the VCPs are mostly in areas with small elevation differences and are not influenced by vegetation and buildings. A three‐step approach generates the 3D coordinates of tie points based on forward intersection, calculates their mean values and finally selects optimal VCPs using a threshold value. Experiments conducted using ZY3‐01 satellite images show the accuracy of the proposed method without GCPs outperforms the existing method.