Analysis of Dualband FLIR Imagery for Automatic Target Detection

Chan, Lipchen Alex, U.S. Army Research Lab.; Der, Sandor Z., U.S. Army Research Lab.; Nasrabadi, Nasser M., U.S. Army Research Lab.

doi:10.1117/3.PM91.ch1

Book: Smart Imaging Systems

Editor(s): Bahram Javidi

Author(s): Lipchen Chan, Sandor Der, Nasser Nasrabadi

Published: 2001

https://doi.org/10.1117/3.PM91.ch1

Author Affiliations +

Abstract

Target detection and recognition are two important modules in a typical automatic target recognition (ATR) system. Usually, an automatic target detector produces many false alarms, necessitating a good clutter rejector to eliminate false alarms before feeding the most likely target detections to the recognizer. We investigate the benefits of using dualband forward-looking infrared (FLIR) images to improve the performance of an eigen-neural-based clutter rejector. With individual or combined bands as input, we use either principal component analysis (PCA) or the eigenspace separation transform (EST) to perform feature extraction and dimensionality reduction. The transformed data is then fed to a multilayer perceptron (MLP) that predicts the identity of the input, which is either a target or clutter. We devise an MLP training algorithm that seeks to maximize the class separation at a given false-alarm rate, which does not necessarily minimize the average deviation of the MLP outputs from their target values. Experiments were conducted based on a dataset of realistic dualband images. The results indicate that the dualband input images do improve the performance of the clutter rejector, especially when the target location and viewing range estimated by the prior detector deviate from the ground-truth information.