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Detecting landmines with ground-penetrating radar using feature-based rules, order statistics, and adaptive whitening

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3 Author(s)
Gader, P. ; Dept. of Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL, USA ; Wen-Hsiung Lee ; Wilson, J.N.

An approach to detecting landmines using ground-penetrating radar (GPR) based on feature-based rules, order statistics, and adaptive whitening (FROSAW) is described. FROSAW relies on independent adaptation of whitening statistics in different depths and combining feature-based methods with anomaly detection using rules. Constant false alarm rate (CFAR) detectors are used for anomaly detection on the depth-dependent adaptively whitened data. A single CFAR confidence measure is computed via a function of order statistics. Anomalies are detected at locations with high CFAR confidence. Depth-dependent features are computed on regions containing anomalies. Rules based on the features are used to reject alarms that do not exhibit mine-like properties. The utility of combining the CFAR and feature-based methods is evaluated. The algorithms and analysis are applied to data acquired from tens of thousands of square meters from several outdoor test sites with a state-of-the-art array of GPR sensors.

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:42 ,  Issue: 11 )