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.
We consider a method to improve the standard RX algorithm for point target detection. In this algorithm, we weight the results from an anti-mean or anti-median filter by dividing by the standard deviation of the local environment of each suspect pixel. In this way, we lower the false alarms caused by edge points. The results of each band are then combined. Results will be shown for visible and SWIR hyperspectral imagery.
In this paper, we apply highly ordered statistics filters to hyperspectral data to enable the detection of anomalous targets whose signatures are known. Each frame has subtracted from it an estimate based on an ordered statistics filter; the resulting frames are then combined optimally based on the covariance data of the cube and the spectral signature of the target. We show that the effect of the ordered statistic filter is to eliminate false alarms at edge points.