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We address the problem of unexploded ordnance (UXO) detection in which data to be classified is available from multiple sensor modalities and multiple resolutions. Specifically, features are extracted from measured magnetometer and electromagnetic induction data; multiple-resolution data are manifested when the sensors are separated from the buried targets of interest by different distances (e.g., different sensor-platform heights). The proposed classification algorithm explicitly emphasizes features extracted from fine-resolution imagery over those extracted from less reliable coarse-resolution data. When fine-resolution features are unavailable (due to undeployed sensors), the algorithm analytically integrates out the missing features via an estimated conditional density function, which is conditioned on the observed features (from deployed sensors). This density function exploits the statistical relationships that exist among features at different resolutions, as well as those among features from different sensors (in the multisensor case). Experimental classification results are shown for real UXO data, on which the proposed algorithm consistently achieves better classification performance than common alternative approaches.