This paper presents a model-based shadow estimation method that aims at identifying self-cast shadows of aerial vehicles in on-board images. The self-cast shadow poses a non-negligible problem in any kind of on-board processing, e.g. remote sensing, visual odometry, or target tracking. Therefore, it often is necessary to exclude the image regions that contain this self-cast shadow. The presented method achieves this exclusion by using data from an INS (Inertial Navigation System) combined with the knowledge of the appearance of the shadow- casting object. This paper will present the self-cast shadow detection algorithm in detail. Further, the algorithm will be tested on flight data that have been recorded by an unmanned helicopter which is operated by the German Aerospace Center. The results show that it is possible to forecast the position of the shadow with an accuracy of over 95 %, thus this method is capable of finding an image region where typical image motion estimation algorithms are likely to fail.
Finding self-cast shadows in aerial camera images
2017-06-01
979201 byte
Conference paper
Electronic Resource
English
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