Reducing false alarms in hyperspectral images using a covariance matrix based on preliminary false detections

Point target detection in hyperspectral data is often plagued by the inability to distinguish between the target and a (relatively) few false alarms. Even when, overall, the signal to noise ratio (SNR) to the overall data is good, the false alarms render use of many detection algorithms problematic. To solve this problem, we propose a two-step process for analyzing the data. We start by performing the standard matched filter (MF) algorithm. While the original covariance matrix is based on all the pixels in the hyperspectral cube, a second covariance matrix is constructed based on the highest detections. Running the algorithm a second time on the original data with this new covariance matrix, we distinguish between the targets and these background false detections. This new method was tested on real world test data and compared to traditional matched filter method results. In all cases, the new method showed a significant decrease in false alarms. Other benchmark metrics show the efficacy of this method.