Optimization of a multi-stage ATR system for small target identification
2010
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.
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