Aerial Wide-Area Motion Imagery Registration Using Automated Multiscale Feature Selection

Automatic registration of aerial wide-area motion imagery is required to correct the camera parameters in order to eliminate the geocoding errors arising from frequent reinstallation of the camera array on the carrier aircraft. Approaches developed to date solely rely on the information present in t...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2018-10, Vol.15 (10), p.1620-1624
Main Authors: Volkova, Anastasiia, Gibbens, Peter W.
Format: Article
Language:English
Subjects:
Aerial image registration
Automation
Cameras
Control methods
Corrections
data association
Datum (elevation)
Feature extraction
Ground based control
Image resolution
Imagery
Optimization
Order parameters
remote sensing
Roads
Robustness
Skeleton
Splines (mathematics)
urban monitoring
Vehicle dynamics
vehicle tracking
wide-area motion imagery (WAMI)
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Description
Summary:Automatic registration of aerial wide-area motion imagery is required to correct the camera parameters in order to eliminate the geocoding errors arising from frequent reinstallation of the camera array on the carrier aircraft. Approaches developed to date solely rely on the information present in the imagery not using a priori knowledge about the environment and the features present in it in the sequence analysis. To this end, we propose a novel method based on dynamic feature extraction and automatic multiscale feature matching to produce per-frame camera pose corrections. The features are extracted from the imagery using one of the three dedicated classifiers and are then robustly matched to the features projected from the datum using a coarse-to-fine multiscale approach. Finally, the bias between the estimated and the actual camera pose is estimated using ordinary least squares optimization performed on the distances between the approved match candidate pairs. The application of the proposed method to 50 frames of very high-resolution aerial imagery captured over mixed terrain at an altitude of 5.3 km demonstrates significant reduction in position error of the features (from 47.76 to 12.31 m) and proves the attractiveness of the approach as an alternative to manual labeling methods using ground control points.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2018.2841845