Premium
Automated Registration of SfM‐MVS Multitemporal Datasets Using Terrestrial and Oblique Aerial Images
Author(s) -
Parente Luigi,
Chandler Jim H.,
Dixon Neil
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.12346
Subject(s) - scale invariant feature transform , photogrammetry , artificial intelligence , computer science , computer vision , remote sensing , image registration , software , scale (ratio) , geography , feature extraction , cartography , image (mathematics) , programming language
Accurate alignment of 3D models is critical for valid change‐detection analysis from multitemporal photogrammetric datasets. This paper assesses an automated registration strategy which uses the scale‐invariant feature transform (SIFT) algorithm implemented in modern photogrammetric software. This registration solution, also known as “Time‐SIFT”, was tested at two study sites featuring vertical surfaces, including a sea cliff (~500 m 2 ) and a quarry face (~50 000 m 2 ). Tests demonstrated that the investigated registration strategy can achieve accurate alignments between multitemporal point clouds even when using multisource and multi‐perspective data, captured across widely varying spatial and temporal scales and under a range of weather and illumination conditions. The combination of the Time‐SIFT approach with an ICP algorithm produced moderate improvements in the alignment. Furthermore, the use of an innovative direct georeferencing technique, which used the tracking feature of a robotic total station, allowed for accurate georectification of 3D models.