Infrared-image classification using support vector machines

Shaorong Chang; Nasser Nasrabadi; Lawrence Carin

doi:10.1109/ICASSP.2002.5745606

Infrared-image classification using support vector machines

Shaorong Chang, Nasser Nasrabadi, Lawrence Carin

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

A target recognition classifier for forward-looking infrared (FLIR) imagery is developed. A target class is defined as a set of contiguous target-sensor orientations (aspects) for which the associated FLIR imagery is stationary. We designed four sets of templates for each target class, to represent the overall image as well as three class-dependent subcomponents. The templates are designed by using expansion matching (EXM) filters and the Karhunen-Loeve transform (KLT). The feature vectors obtained with these eigen templates are used in the context of a support vector machine (SVM). The performance of the SVM classifier is presented and compared with other competitive classifiers.

Original language	English (US)
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	4
DOIs	https://doi.org/10.1109/ICASSP.2002.5745606
State	Published - Jan 1 2002
Externally published	Yes

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10.1109/ICASSP.2002.5745606

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abstract = "A target recognition classifier for forward-looking infrared (FLIR) imagery is developed. A target class is defined as a set of contiguous target-sensor orientations (aspects) for which the associated FLIR imagery is stationary. We designed four sets of templates for each target class, to represent the overall image as well as three class-dependent subcomponents. The templates are designed by using expansion matching (EXM) filters and the Karhunen-Loeve transform (KLT). The feature vectors obtained with these eigen templates are used in the context of a support vector machine (SVM). The performance of the SVM classifier is presented and compared with other competitive classifiers.",

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AU - Carin, Lawrence

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N2 - A target recognition classifier for forward-looking infrared (FLIR) imagery is developed. A target class is defined as a set of contiguous target-sensor orientations (aspects) for which the associated FLIR imagery is stationary. We designed four sets of templates for each target class, to represent the overall image as well as three class-dependent subcomponents. The templates are designed by using expansion matching (EXM) filters and the Karhunen-Loeve transform (KLT). The feature vectors obtained with these eigen templates are used in the context of a support vector machine (SVM). The performance of the SVM classifier is presented and compared with other competitive classifiers.

AB - A target recognition classifier for forward-looking infrared (FLIR) imagery is developed. A target class is defined as a set of contiguous target-sensor orientations (aspects) for which the associated FLIR imagery is stationary. We designed four sets of templates for each target class, to represent the overall image as well as three class-dependent subcomponents. The templates are designed by using expansion matching (EXM) filters and the Karhunen-Loeve transform (KLT). The feature vectors obtained with these eigen templates are used in the context of a support vector machine (SVM). The performance of the SVM classifier is presented and compared with other competitive classifiers.

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JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

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Infrared-image classification using support vector machines

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