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Automatic Rough Georeferencing of Multiview Oblique and Vertical Aerial Image Datasets of Urban Scenes
Author(s) -
Verykokou Styliani,
Ioannidis Charalabos
Publication year - 2016
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.12156
Subject(s) - oblique case , computer vision , georeference , artificial intelligence , computer science , aerial image , matching (statistics) , suite , feature (linguistics) , representation (politics) , perspective (graphical) , metadata , ground sample distance , software , image (mathematics) , computer graphics (images) , remote sensing , geography , pixel , mathematics , philosophy , linguistics , statistics , archaeology , physical geography , politics , political science , law , programming language , operating system
Multi‐perspective airborne images that combine oblique and vertical views of the ground have proved to be a valuable source of information for numerous applications requiring a digital representation of the world. In this paper, an automatic methodology for rough georeferencing of large datasets of multiview oblique and vertical aerial images of urban regions without any metadata is proposed. Using feature‐based matching combined with robust model fitting and least‐squares techniques, the method requires the measurement of a minimum number of points with known coordinates in only one image. The results of this methodology are discussed through the presentation of a developed software suite which identifies the overlapping images, georeferences them, extracts their footprints, subdivides the images into groups based on these footprints and detects the images that cover a specific region.