30 April 2018 Detecting explosive hazards in 3D radar imaging through clustering and sequential learning
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Abstract
In this paper, we present a methodology for detecting side-attack explosive hazards using three-dimensional radar imaging. Our methodology is based on clustering intensities of voxel cubes extracted around points of interest generated from prescreener filters. To make our computations easier, and results visually comprehensible, we break our voxel cubes into slices within the x-, y-, and z-directions of equal dimensions. With these slices, we explore various feature extraction algorithms: K-Means, Fuzzy C-Means (FCM), and statistical moments on the radar intensity slices to create feature vectors based on a set number of cluster centers and number of slices within the extracted cubes. We evaluate the performance of the features produced using Hidden Markov Model (HMM) classifiers on a set of lane data supplied by the US Army.
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Q. LaRoe, Q. LaRoe, M. Popescu, M. Popescu, J. M. Keller, J. M. Keller, "Detecting explosive hazards in 3D radar imaging through clustering and sequential learning", Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 1062817 (30 April 2018); doi: 10.1117/12.2304552; https://doi.org/10.1117/12.2304552
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