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A Study on Clutter Rejection using PCA and Stochastic features of Edge Image  

Kang, Suk-Jong (Agency for Defense development, 5th(Ground Systems) R&D Institute)
Kim, Do-Jong (Agency for Defense development, 5th(Ground Systems) R&D Institute)
Bae, Hyeon-Deok (Department of Electrical Engineering Chungbuk National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.47, no.6, 2010 , pp. 12-18 More about this Journal
Abstract
Automatic Target Detection (ATD) systems that use forward-looking infrared (FLIR) consists of three stages. preprocessing, detection, and clutter rejection. All potential targets are extracted in preprocessing and detection stages. But, this results in a high false alarm rates. To reduce false alarm rates of ATD system, true targets are extracted in the clutter rejection stage. This paper focuses on clutter rejection stage. This paper presents a new clutter rejection technique using PCA features and stochastic features of clutters and targets. PCA features are obtained from Euclidian distances using which potential targets are projected to reduced eigenspace selected from target eigenvectors. CV is used for calculating stochastic features of edges in targets and clutters images. To distinguish between target and clutter, LDA (Linear Discriminant Analysis) is applied. The experimental results show that the proposed algorithm accurately classify clutters with a low false rate compared to PCA method or CV method
Keywords
pricipal component analysis; coefficients of variance; clutter rejection; edge image;
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