Robust, timely, and remote detection of mines and minefields is central to both tactical and humanitarian demining efforts, yet remains elusive for single-sensor systems. Here we present an approach to jointly exploit multisensor data for detection of mines from remotely sensed imagery. LWIR, MWIR, laser, multispectral, and radar sensor have been applied individually to the mine detection and each has shown promise for supporting automated detection. However, none of these sources individually provides a full solution for automated mine detection under all expected mine, background and environmental conditions. Under support from Night Vision and Electronic Sensors Directorate (NVESD) we have developed an approach that, through joint exploitation of multiple sensors, improves detection performance over that achieved from a single sensor. In this paper we describe the joint exploitation method, which is based on fundamental detection theoretic principles, demonstrate the strength of the approach on imagery from minefields, and discuss extensions of the method to additional sensing modalities. The approach uses pre-threshold anomaly detector outputs to formulate accurate models for marginal and joint statistics across multiple detection or sensor features. This joint decision space is modeled and decision boundaries are computed from measured statistics. Since the approach adapts the decision criteria based on the measured statistics and no prior target training information is used, it provides a robust multi-algorithm or multisensor detection statistic. Results from the joint exploitation processing using two different imaging sensors over surface mines acquired by NVESD will be presented to illustrate the process. The potential of the approach to incorporate additional sensor sources, such as radar, multispectral and hyperspectral imagery is also illustrated.