20 April 2015 Machine vision for airport runway identification
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For rigid objects and fixed scenes, current machine vision technology is capable of identifying imagery rapidly and with specificity over a modest range of camera viewpoints and scene illumination. We applied that capability to the problem of runway identification using video of sixteen runway approaches at nine locations, subject to two simplifying assumptions. First, by using approach video from just one of the several possible seasonal variations (no snow cover and full foliage), we artificially removed one source of scene variation in this study. Secondly, by not using approach video at dawn and dusk, we limited the study to two illumination variants (day and night). We did allow scene variation due to atmospheric turbidity by using approach video from rainy and foggy days in some daytime approaches. With suitable ensemble statistics to account for temporal continuity in video, we observed high location specificity (<90% Bayesian posterior probability). We also tested repeatability, i.e., identification of a given runway across multiple videos, and observed robust repeatability only if illumination (day vs. night) was the same and approach visibility was good. Both specificity and repeatability degraded in poor weather conditions. The results of this simplified study show that geolocation via real-time comparison of cockpit image sensor video to a database of runway approach imagery is feasible, as long as the database contains imagery from about the same time of day (complete daylight and nighttime, excluding dawn and dusk) and the weather is clear at the time of the flight.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Matthew Schubert, Matthew Schubert, Andrew J. Moore, Andrew J. Moore, Chester Dolph, Chester Dolph, Glenn Woodell, Glenn Woodell, "Machine vision for airport runway identification", Proc. SPIE 9477, Optical Pattern Recognition XXVI, 94770G (20 April 2015); doi: 10.1117/12.2177320; https://doi.org/10.1117/12.2177320


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