Prediction markets have been shown to be a useful tool in forecasting the outcome of future events by aggregating public opinion about the event’s outcome. We consider an analogous problem of information fusion from multiple sensors of different types with the objective of improving the confidence of inference tasks, such as object classification. We develop a multi-agent prediction market-based technique to solve this information fusion problem. To monitor the improvement in the confidence of the object classification as well as to dis-incentivize agents from misreporting information, we have introduced a market maker that rewards the agents based on the quality of the submitted reports. We have implemented the market maker’s reward calculation in the form of a scoring rule and have shown analytically that it incentivizes truthful revelation by each agent. We have experimentally verified our technique for multi-sensor information fusion for an automated landmine detection scenario. Our experimental results show that, for identical data distributions and settings, using our information aggregation technique increases the accuracy of object classification favorably as compared to two other commonly used techniques for information fusion for landmine detection.