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17 August 2000 Application of dualband infrared imagery in automatic target detection
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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 that could incur very poor recognition performance in the subsequent target recognizer. Therefore, we need a good clutter rejector to remove as many clutters as possible from the outputs of the detector, 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 a 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 an 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. Experimental results are presented on a dataset of real dualband images.
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Lipchen Alex Chan, Sandor Z. Der, and Nasser M. Nasrabadi "Application of dualband infrared imagery in automatic target detection", Proc. SPIE 4050, Automatic Target Recognition X, (17 August 2000);


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