Paper
29 May 2014 Operator based integration of information in multimodal radiological search mission with applications to anomaly detection
J. Benedetto, A. Cloninger, W. Czaja, T. Doster, K. Kochersberger, B. Manning, T. McCullough, M. McLane
Author Affiliations +
Abstract
Successful performance of radiological search mission is dependent on effective utilization of mixture of signals. Examples of modalities include, e.g., EO imagery and gamma radiation data, or radiation data collected during multiple events. In addition, elevation data or spatial proximity can be used to enhance the performance of acquisition systems. State of the art techniques in processing and exploitation of complex information manifolds rely on diffusion operators. Our approach involves machine learning techniques based on analysis of joint data- dependent graphs and their associated diffusion kernels. Then, the significant eigenvectors of the derived fused graph Laplace and Schroedinger operators form the new representation, which provides integrated features from the heterogeneous input data. The families of data-dependent Laplace and Schroedinger operators on joint data graphs, shall be integrated by means of appropriately designed fusion metrics. These fused representations are used for target and anomaly detection.
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J. Benedetto, A. Cloninger, W. Czaja, T. Doster, K. Kochersberger, B. Manning, T. McCullough, and M. McLane "Operator based integration of information in multimodal radiological search mission with applications to anomaly detection", Proc. SPIE 9073, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XV, 90731A (29 May 2014); https://doi.org/10.1117/12.2050721
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Cited by 6 scholarly publications.
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KEYWORDS
Gamma radiation

Information fusion

Sensors

Target detection

Data acquisition

Machine learning

Radon

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