Paper
7 May 2007 Precise estimation of pose for vehicles in MSTAR imagery
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Abstract
A new algorithm for pose estimation of vehicles in SAR imagery is presented. Using robust features and a structured decision process, the algorithm achieves high precision. Four neural networks are used to make estimates conditional on angular regions, and another neural network is used to fuse these estimates. For the MSTAR Test Sample, the absolute error has a mean of 2 degrees with a standard deviation of 2.1, which is significantly more precise than previously reported results.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Frank McFadden "Precise estimation of pose for vehicles in MSTAR imagery", Proc. SPIE 6566, Automatic Target Recognition XVII, 65660V (7 May 2007); https://doi.org/10.1117/12.718140
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Cited by 1 scholarly publication.
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KEYWORDS
Error analysis

Neural networks

Image analysis

Automatic target recognition

Network architectures

Statistical modeling

Synthetic aperture radar

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