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Proceedings Paper

Optimization of a multi-stage ATR system for small target identification
Author(s): Tsung Han (Hank) Lin; Thomas Lu; Henry Braun; Weston Edens; Yuhan Zhang; Tien-Hsin Chao; Christopher Assad; Terrance Huntsberger
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Paper 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.

Paper Details

Date Published: 7 May 2010
PDF: 10 pages
Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 76961Y (7 May 2010); doi: 10.1117/12.858165
Show Author Affiliations
Tsung Han (Hank) Lin, Univ. of California, San Diego (United States)
Thomas Lu, Jet Propulsion Lab. (United States)
Henry Braun, Arizona State Univ. (United States)
Weston Edens, Butler Univ. (United States)
Purdue Univ. (United States)
Yuhan Zhang, Califonia Polytechnic Univ. (United States)
Tien-Hsin Chao, Jet Propulsion Lab. (United States)
Christopher Assad, Jet Propulsion Lab. (United States)
Terrance Huntsberger, Jet Propulsion Lab. (United States)


Published in SPIE Proceedings Vol. 7696:
Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
Firooz A. Sadjadi; David P. Casasent; Steven L. Chodos; Abhijit Mahalanobis; William E. Thompson; Tien-Hsin Chao, Editor(s)

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