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Proceedings Paper

Improved target detection algorithm using dualband infrared imagery
Author(s): Lipchen Alex Chan; Sandor Z. Der; Nasser M. Nasrabadi
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Paper Abstract

Automatic target recognition (ATR) systems typically include a front-end detector that searches the entire image and isolates those regions which potentially contain targets. These regions are further analyzed by more complex recognition algorithms. A good detector will find most of the targets with relatively few false alarms, at reasonable computational cost. We propose a set of neural-based target detectors consisting of an eigenspace transformation followed by a simple multilayer perceptron (MLP). These detectors may operate on either single band or multi-band forward-looking infrared (FLIR) input images. The eigenspace transformation is first derived from a training set via principal component analysis (PCA). This transformation layer is needed to reduce the dimensionality of the input images, while retaining those features that are critical to the detection task. A small number of the resulting projection values is then fed to the MLP that determines the likelihood of a given pixel location to be the center of a target. Experiments were conducted using a set of several hundred real FLIR images. The results indicate that the dualband input images do improve the performance of the detectors substantially.

Paper Details

Date Published: 22 October 2001
PDF: 12 pages
Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); doi: 10.1117/12.445365
Show Author Affiliations
Lipchen Alex Chan, Army Research Lab. (United States)
Sandor Z. Der, Army Research Lab. (United States)
Nasser M. Nasrabadi, Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 4379:
Automatic Target Recognition XI
Firooz A. Sadjadi, Editor(s)

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