An Adaptive Threshold Method for Hyperspectral Target Detection

In this paper, we present a new approach to automatically determine a detector threshold. This research problem is especially important in hyperspectral target detection as targets are typically very similar to the background. While a number of methods exist to determine the threshold, these methods require either large amounts of data or make simplifying assumptions about the background distribution. We use a method called inverse blind importance sampling which requires few samples and makes no a-priori assumptions about the background statistics. Results show the promise of this algorithm to determine thresholds for fixed false alarm densities in hyperspectral detectors