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Assessment of dense image matchers for digital surface model generation using airborne and spaceborne images – an update
Author(s) -
Han Yilong,
Qin Rongjun,
Huang Xu
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.12310
Subject(s) - photogrammetry , remote sensing , software , aerial image , computer science , aerial survey , matching (statistics) , artificial intelligence , lidar , computer vision , block (permutation group theory) , digital image , digital surface , image (mathematics) , image processing , geology , mathematics , statistics , geometry , programming language
Digital surface model (DSM) generation is one of the fundamental issues in photogrammetry and the mapping industry. This paper provides a comprehensive assessment of state‐of‐the‐art image matchers using nine open‐source and commercial software packages on aerial and unmanned aerial vehicle (UAV) images and five software packages on spaceborne images. Two datasets provide an update on DSM generation software for both airborne and spaceborne data: a 5 × 5 UAV image block with high‐precision models; and a WorldView‐1 stereopair with lidar reference data. To understand the performance of the image matchers, accuracy analysis is additionally performed on five selected ground objects. The tested image matchers adopting hierarchical semi‐global matching fitted the reference DSM better, thus yielding better accuracy.