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A technique for making landmine detection with a ground-penetrating radar (GPR) sensor more robust to fluctuations in environmental conditions is presented. Context-dependent feature selection (CDFS) counteracts environmental uncertainties that degrade detection and discrimination performances by modifying decision rules based on inference of the environmental context. This paper utilized both physics-based and statistical methods for extracting features from GPR data to characterize surface texture and subsurface electrical properties, and a nonparametric hypothesis test was used to identify the environmental context from which the data were collected. The results of probabilistic context identification were then used to fuse an ensemble of classifiers for discriminating landmines from clutter under diverse environmental conditions. CDFS was evaluated on a large set of GPR data collected over several years in different weather and terrain conditions. Results indicate that our context-dependent technique improved landmine discrimination performance over conventional fusion of several currently fielded algorithms from the recent literature.