Hyperspectral performance prediction of the adaptive cosine estimator

The adaptive cosine estimator is a popular and effective algorithm for detecting materials in hyperspectral images. To predict the performance of this algorithm in real hyperspectral scenes, a statistical model using a mixture of multivariate t-distributions for the background and a Gaussian distribution for the target is utilized. In this paper, two methods for finding the response of the adaptive cosine estimator (ACE) and Beta-detector when applied to a statistical model. To verify that the proposed techniques work as expected, t-distribution and F-distribution quantiles are computed and compared to standard values. Finally, a preliminary validation with Monte Carlo simulation based on real hyperspectral data is presented. We build on previous work for the matched filter and extends it to use two more detectors.