29 April 2010 Comparison of different classification algorithms for landmine detection using GPR
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The Edge Histogram Detector (EHD) is a landmine detection algorithm that has been developed for ground penetrating radar (GPR) sensor data. It has been tested extensively and has demonstrated excellent performance. The EHD consists of two main components. The first one maps the raw data to a lower dimension using edge histogram based feature descriptors. The second component uses a possibilistic K-Nearest Neighbors (pK-NN) classifier to assign a confidence value. In this paper we show that performance of the baseline EHD could be improved by replacing the pK-NN classifier with model based classifiers. In particular, we investigate two such classifiers: Support Vector Regression (SVR), and Relevance Vector Machines (RVM). We investigate the adaptation of these classifiers to the landmine detection problem with GPR, and we compare their performance to the baseline EHD with a pK-NN classifier. As in the baseline EHD, we treat the problem as a two class classification problem: mine vs. clutter. Model parameters for the SVR and the RVM classifiers are estimated from training data using logarithmic grid search. For testing, soft labels are assigned to the test alarms. A confidence of zero indicates the maximum probability of being a false alarm. Similarly, a confidence of one represents the maximum probability of being a mine. Results on large and diverse GPR data collections show that the proposed modification to the classifier component can improve the overall performance of the EHD significantly.
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Andrew Karem, Andrew Karem, Aleksey Fadeev, Aleksey Fadeev, Hichem Frigui, Hichem Frigui, Paul Gader, Paul Gader, "Comparison of different classification algorithms for landmine detection using GPR", Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 76642K (29 April 2010); doi: 10.1117/12.852257; https://doi.org/10.1117/12.852257

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