One of the fundamental challenges for a hyperspectral imaging surveillance system is the detection of sub-pixel objects in background clutter. The background surrounding the object, which acts as interference, provides the major obstacle to successful detection. One algorithm that is widely used in hyperspectral detection and successfully suppresses the background in many situations is the matched filter detector. However, the matched filter also produces false alarms in many situations. We use three simple and well-established concepts-the target-background replacement model, the matched filter, and Mahalanobis distance-to develop the matched filter with false alarm mitigation (MF-FAM), a dual-threshold detector capable of eliminating many matched filter false alarms. We compare this algorithm to the mixture tuned matched filter (MTMF), a popular approach to matched filter false alarm mitigation found in the ENVI® software environment. The two algorithms are shown to produce nearly identical results using real hyperspectral data, but the MF-FAM is shown to be operationally, computationally, and theoretically simpler than the MTMF.