Statistical methods for analysis of hyperspectral anomaly detectors

Abstract

Most hyperspectral (HS) anomaly detectors in the literature have been evaluated using a few HS imagery sets to estimate the well-known ROC curve. Although this evaluation approach can be helpful in assessing detectors' rates of correct detection and false alarm on a limited dataset, it does not shed lights on reasons for these detectors' strengths and weaknesses using a significantly larger sample size. This paper discusses a more rigorous approach to testing and comparing HS anomaly detectors, and it is intended to serve as a guide for such a task. Using randomly generated samples, the approach introduces hypothesis tests for two idealized homogeneous sample experiments, where model parameters can vary the difficulty level of these tests. These simulation experiments are devised to address a more generalized concern, i.e., the expected degradation of correct detection as a function of increasing noise in the alternative hypothesis.