Spectral signatures derived from a multispectral or hyperspectral imager can be used in matched filter algorithms to help distinguish targets from background. In this paper we demonstrate the use of these matched filters for different target implantation models. We show that even though a specific matched filter is designated for a particular implantation model, we can use other matched filters and obtain higher detection values for low false alarm rates. We evaluate the efficiency of the algorithms by systematically implanting the target's signature into every pixel in the image and obtaining its score; the lowest scores are those pixels in which the target may be missed. For every algorithm, we generate histograms for the no-target and target cases and then analyze using the classical ROC curve.