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Proceedings Paper

Decision-level fusion of SAR and IR sensor information for automatic target detection
Author(s): Young-Rae Cho; Sung-Hyuk Yim; Hyun-Woong Cho; Jin-Ju Won; Woo-Jin Song; So-Hyeon Kim
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Paper Abstract

We propose a decision-level architecture that combines synthetic aperture radar (SAR) and an infrared (IR) sensor for automatic target detection. We present a new size-based feature, called target-silhouette to reduce the number of false alarms produced by the conventional target-detection algorithm. Boolean Map Visual Theory is used to combine a pair of SAR and IR images to generate the target-enhanced map. Then basic belief assignment is used to transform this map into a belief map. The detection results of sensors are combined to build the target-silhouette map. We integrate the fusion mass and the target-silhouette map on the decision level to exclude false alarms. The proposed algorithm is evaluated using a SAR and IR synthetic database generated by SE-WORKBENCH simulator, and compared with conventional algorithms. The proposed fusion scheme achieves higher detection rate and lower false alarm rate than the conventional algorithms.

Paper Details

Date Published: 2 May 2017
PDF: 9 pages
Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102001D (2 May 2017); doi: 10.1117/12.2262271
Show Author Affiliations
Young-Rae Cho, Pohang Univ. of Science and Technology (Korea, Republic of)
Sung-Hyuk Yim, Pohang Univ. of Science and Technology (Korea, Republic of)
Hyun-Woong Cho, Pohang Univ. of Science and Technology (Korea, Republic of)
Jin-Ju Won, Yeungnam Univ. (Korea, Republic of)
Woo-Jin Song, Pohang Univ. of Science and Technology (Korea, Republic of)
So-Hyeon Kim, Agency for Defense Development (Korea, Republic of)


Published in SPIE Proceedings Vol. 10200:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI
Ivan Kadar, Editor(s)

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