Fusion of Quadratic Detection Statistics Applied to Hyperspectral Imagery

摘要

A variety of detection statistics have been developed and applied to hyperspectral imagery (HSI). The Reed Xiaoli (RX) algorithm is a generalized likelihood ratio test (GLRT) that uses local estimates of the spectral mean and spectral covariance. It satisfies an optimality criterion if locally the spectral data have a multivariate normal probability distribution. Alternatively, the stochastic expectation maximization (SEM) algorithm may be used to estimate the spectral mean values and spectral covariance matrices of a pre-determined number of classes. A detection statistic is computed by identifying each pixel with the class having maximal a posteriori probability and applying the GLRT detection statistic for that class. These algorithms are based on different models and provide different information about the imagery.