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Feature Extraction and Selection Strategies for Automated Target RecognitionSeveral 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.
Document ID
20150008574
Document Type
Conference Paper
External Source(s)
Authors
Greene, W. Nicholas (Princeton Univ. Princeton, NJ, United States)
Zhang, Yuhan (California Polytechnic Univ. Pomona, CA, United States)
Lu, Thomas T. (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Chao, Tien-Hsin (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
May 20, 2015
Publication Date
April 5, 2010
Subject Category
Optics
Meeting Information
SPIE Defense, Security & Sensing(Orlando, FL)
Distribution Limits
Public
Copyright
Other
Keywords
computer vision
pattern recognition