We describe a methodology for multiframe image registration of airborne high resolution, multi-camera imagery. In the
absence of predetermined camera and lens models, parameters are optimally determined from imagery and known
ground reference locations. GPS and IMU data collected from the sensor platform and the identified camera model
parameters are used to perform an initial orthorectification and georeferencing of each image. Multiple KLT, Sift, or
featureless point-match correspondences are identified and validated using RANSAC techniques. Affine transform
hypothesis are then generated, inconsistent hypothesis are removed using a RANSAC approach, and a final optimal
transform is generated as the least squares optimal fit of the remaining correspondences. To eliminate long-term drift,
key frames are selected and cross-registered. Performance improvements can also be demonstrated using a mask to
eliminate correspondences not on the ground plane. This approach is illustrated using the 2007 AFRL Columbus Large
Image Format dataset.
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