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Optimization of a multi-stage ATR system for small target identification

Show simple item record Lin, Tsung Han (Hank) Lu, Thomas Braun, Henry Edens, Western Zhang, Yuhan Chao, Tien- Hsin Assad, Christopher Huntsberger, Terrance 2014-09-26T21:42:27Z 2014-09-26T21:42:27Z 2010-04-05
dc.identifier.citation SPIE Defense, Security & Sensing, Orlando, Florida, April 5-9, 2010 en_US
dc.identifier.clearanceno 10-0635
dc.description.abstract An Automated Target Recognition system (ATR) was developed to locate and target small object in images and videos. The data is preprocessed and sent to a grayscale optical correlator (GOC) filter to identify possible regionsof- interest (ROIs). Next, features are extracted from ROIs based on Principal Component Analysis (PCA) and sent to neural network (NN) to be classified. The features are analyzed by the NN classifier indicating if each ROI contains the desired target or not. The ATR system was found useful in identifying small boats in open sea. However, due to “noisy background,” such as weather conditions, background buildings, or water wakes, some false targets are mis-classified. Feedforward backpropagation and Radial Basis neural networks are optimized for generalization of representative features to reduce false-alarm rate. The neural networks are compared for their performance in classification accuracy, classifying time, and training time. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2010 en_US
dc.subject correlation en_US
dc.subject neural network en_US
dc.subject false alarm rate en_US
dc.subject small target en_US
dc.title Optimization of a multi-stage ATR system for small target identification en_US
dc.type Preprint en_US

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