3 May 2016 Convolutional neural network based sensor fusion for forward looking ground penetrating radar
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Abstract
Forward looking ground penetrating radar (FLGPR) is an alternative buried threat sensing technology designed to offer additional standoff compared to downward looking GPR systems. Due to additional flexibility in antenna configurations, FLGPR systems can accommodate multiple sensor modalities on the same platform that can provide complimentary information. The different sensor modalities present challenges in both developing informative feature extraction methods, and fusing sensor information in order to obtain the best discrimination performance. This work uses convolutional neural networks in order to jointly learn features across two sensor modalities and fuse the information in order to distinguish between target and non-target regions. This joint optimization is possible by modifying the traditional image-based convolutional neural network configuration to extract data from multiple sources. The filters generated by this process create a learned feature extraction method that is optimized to provide the best discrimination performance when fused. This paper presents the results of applying convolutional neural networks and compares these results to the use of fusion performed with a linear classifier. This paper also compares performance between convolutional neural networks architectures to show the benefit of fusing the sensor information in different ways.
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Rayn Sakaguchi, Rayn Sakaguchi, Miles Crosskey, Miles Crosskey, David Chen, David Chen, Brett Walenz, Brett Walenz, Kenneth Morton, Kenneth Morton, } "Convolutional neural network based sensor fusion for forward looking ground penetrating radar", Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98231J (3 May 2016); doi: 10.1117/12.2224125; https://doi.org/10.1117/12.2224125
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