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10 June 1996 Feature space trajectory (FST) neural network for SAR detection, classification, and clutter rejection
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Abstract
We consider use of eigenvector feature inputs to our feature space trajectory (FST) neural net classifier for SAR data with 3D aspect distortions. We consider its use for classification and pose estimation and rejection of clutter. Prior and new MINACE distortion-invariant and shift- invariant filter work to locate the position of objects in regions of interest is reviewed. Test results on a number of SAR databases are included to show the robustness of the algorithm. New results include techniques to determine: the number of eigenvectors per class to retain, the number and order of final features to use, if the training set size is adequate, and if the training and test sets are compatible.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David P. Casasent, Rajesh Shenoy, and Leonard Neiberg "Feature space trajectory (FST) neural network for SAR detection, classification, and clutter rejection", Proc. SPIE 2757, Algorithms for Synthetic Aperture Radar Imagery III, (10 June 1996); https://doi.org/10.1117/12.242038
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