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A new productive framework for point‐based matching of oblique aircraft and UAV‐based images
Author(s) -
Bas Sıla,
Ok Ali Ozgun
Publication year - 2021
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.12374
Subject(s) - ransac , oblique case , artificial intelligence , matching (statistics) , epipolar geometry , computer science , benchmark (surveying) , context (archaeology) , computer vision , feature (linguistics) , point (geometry) , feature extraction , pattern recognition (psychology) , orientation (vector space) , image (mathematics) , mathematics , geography , cartography , statistics , geometry , archaeology , philosophy , linguistics
A novel productive framework for point‐based feature matching of oblique aircraft and UAV imagery is presented. The proposed framework makes use of the powerful AKAZE descriptor for feature extraction and an iterative scheme is developed to construct as many tentative matches as possible. During the iterations, cross checks, together with Lowe’s nearest‐next distance ratio test, are used to filter erroneous matches. In order to extract putative matches from the tentative matches, three robust approaches, including graph‐cut RANSAC, are evaluated along with the epipolar constraint enforced between the two datasets. The developed framework was validated using the ISPRS image orientation benchmark dataset and yielded successful results in terms of matching precision, even for some difficult cases. The results also outperformed the results of previously developed approaches in the same context.