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Feature extraction and selection strategies for automated target recognition

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dc.contributor.author Greene, W. Nicholas
dc.contributor.author Zhang, Yuhan
dc.contributor.author Lu, Thomas T.
dc.contributor.author Chao, Tien-Hsin
dc.date.accessioned 2014-09-26T21:42:09Z
dc.date.available 2014-09-26T21:42:09Z
dc.date.issued 2010-04-05
dc.identifier.citation SPIE Defense, Security & Sensing, Orlando, Florida, April 5-9, 2010 en_US
dc.identifier.clearanceno 10-0667
dc.identifier.uri http://hdl.handle.net/2014/44791
dc.description.abstract Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier. 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 pattern recognition en_US
dc.subject computer vision en_US
dc.title Feature extraction and selection strategies for automated target recognition en_US
dc.type Preprint en_US


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