We analyze the efficacy of various point target detection algorithms for hyperspectral data. We present a novel way to measure the discrimination capability of a target detection algorithm; we avoid being critically dependent on the particular placement of a target in the image by examining the overall ability to detect a target throughout the various backgrounds of the cube. We first demonstrate this approach by analyzing previously published algorithms from the literature; we then present two new dissimilar algorithms that are designed to eliminate false alarms on edges. Trade-offs between the probability of detection and false alarms rates are considered. We use our metrics to quantify the improved capability of the proposed algorithms over the standard algorithms.