4 May 2009 Adaptive edge histogram descriptor for landmine detection using GPR
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Abstract
The Edge Histogram Detector (EHD) is a landmine detection algorithm for sensor data generated by ground penetrating radar (GPR). It uses edge histograms for feature extraction and a possibilistic K-Nearest Neighbors (K-NN) rule for confidence assignment. To reduce the computational complexity of the EHD and improve its generalization, the K-NN classifier uses few prototypes that can capture the variations of the signatures within each class. Each of these prototypes is assigned a label in the class of mines and a label in the class of clutter to capture its degree of sharing among these classes. The EHD has been tested extensively. It has demonstrated excellent performance on large real world data sets, and has been implemented in real time versions in hand-held and vehicle mounted GPR. In this paper, we propose two modifications to the EHD to improve its performance and adaptability. First, instead of using a fixed threshold to decide if the edge at a certain location is strong enough, we use an adaptive threshold that is learned from the background surrounding the target. This modification makes the EHD more adaptive to different terrains and to mines buried at different depths. Second, we introduce an additional training component that tunes the prototype features and labels to different environments. Results on large and diverse GPR data collections show that the proposed adaptive EHD outperforms the baseline EHD. We also show that the edge threshold can vary significantly according to the edge type, alarm depth, and soil conditions.
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Hichem Frigui, Hichem Frigui, Aleksey Fadeev, Aleksey Fadeev, Andrew Karem, Andrew Karem, Paul Gader, Paul Gader, } "Adaptive edge histogram descriptor for landmine detection using GPR", Proc. SPIE 7303, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIV, 730321 (4 May 2009); doi: 10.1117/12.819436; https://doi.org/10.1117/12.819436
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