2 May 2017 Decision-level fusion of SAR and IR sensor information for automatic target detection
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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.
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Young-Rae Cho, Young-Rae Cho, Sung-Hyuk Yim, Sung-Hyuk Yim, Hyun-Woong Cho, Hyun-Woong Cho, Jin-Ju Won, Jin-Ju Won, Woo-Jin Song, Woo-Jin Song, So-Hyeon Kim, So-Hyeon Kim, } "Decision-level fusion of SAR and IR sensor information for automatic target detection", Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102001D (2 May 2017); doi: 10.1117/12.2262271; https://doi.org/10.1117/12.2262271
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