The majority of anomaly detection processes used for hyperspectral image data are based on pixel-by-pixel whitening and thresholding operations using local area statistics. This paper discusses an alternative approach to anomaly detection in which a mixture model is fitted to the whole of the image. This mixture model may be used to segment the image into component memberships and these may, in turn, be used for anomaly detection.
In this study the mixture model is generated for the whole scene using the stochastic expectation maximization (SEM) algorithm. This is parameterized such that mixture components consisting of small numbers of pixels are eliminated. The maximum a-posteriori probability (MAP) mixture component for each pixel is then determined. The pixel may then be examined using a conventional statistical hypothesis test to see whether it is plausible that it was drawn from the distribution of the identified component, at a given significance level.
This anomaly detection process has been examined using both synthetic and real hyperspectral imagery and results are presented here for real data containing no known military targets and for synthesized imagery which includes military target pixels. A range of results is presented for different parameterizations of the SEM algorithm and significance test. These results include the component map of the imagery and anomalous pixel maps at given significance levels.