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Image geo-registration is an essential technology with a wide range of applications in the geospatial intelligence space. Prior to recent deep learning advancements, image geo-registration primarily used area-based image correlation algorithms. The “line-photogrammetry” [1] concept, which uses lines instead of points for georegistration and triangulation, has not been widely adapted due to the difficulties of reliably and accurately detecting lines from geospatial images. With deep learning, this is no longer a challenge. At BAE Systems GXP, we have taken advantage of the recent advancements in deep learning and have developed DeepTie, which uses both tie points and tie features, for image geo-registration and triangulation.
DeepTie uses two deep learning models: (1)TieFeature; and (2)TiePoint. In this paper, we focus on the TieFeature model, as tie features provide much richer information than tie points. Analogous to identifying a person, to use the TiePoint model is to only use a person’s nose to identify that person, while the TieFeature model uses the whole face of the person for identification. While the TiePoint model is much faster than the TieFeature model, tie features are much more reliable and accurate than tie points. We have developed tie feature matching algorithms to reliably and accurately match tie features for images which are taken from different years, different seasons, and different sensors.
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