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28 July 1997 Multiclass SAR feature space trajectory (FST) neural network class and pose estimation results
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Abstract
The feature space trajectory representation and neural network is used for classification and pose estimation of distorted objects in SAR data. New feature spaces and techniques to extend the concept to multiple classes are emphasized with initial four class results. On 4 class data, we obtain Pc equals 98.3 percent and clutter PFA equals 0.026/km2.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rajesh Shenoy and David P. Casasent "Multiclass SAR feature space trajectory (FST) neural network class and pose estimation results", Proc. SPIE 3070, Algorithms for Synthetic Aperture Radar Imagery IV, (28 July 1997); https://doi.org/10.1117/12.281549
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