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3D orientation vector estimation for subcomponents of space object imagery

We describe a model-based image analysis system which automatically estimates the 3D orientation vector of satellites and their sub-components by analyzing images obtained from a ground-based optical surveillance system. We adopt a two-step approach: pose estimates are derived from comparisons with a model database; pose refinements are derived from photogrammetric information. The model database is formed by representing each available training image by a set of derived geometric primitives. To obtain fast access to the model database and to increase the probability of early successful matching, a novel index hashing method is introduced. We present recent results which include our efforts at isolating and estimating orientation vectors from degraded imagery on a significant database of satellites. We also discuss the problems our system encounters with some of the images, and the solutions we are implementing to significantly improve the system.