Feature space trajectory (FST) neural network for SAR detection, classification, and clutter rejection

David P. Casasent; Rajesh Shenoy; Leonard Neiberg

doi:10.1117/12.242038

10 June 1996 Feature space trajectory (FST) neural network for SAR detection, classification, and clutter rejection

David P. Casasent, Rajesh Shenoy, Leonard Neiberg

Proceedings Volume 2757, Algorithms for Synthetic Aperture Radar Imagery III; (1996) https://doi.org/10.1117/12.242038
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

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.

Citation Download Citation

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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