Abstract: A method and apparatus are provided for analysing a scene, in particular a scene represented in a hyperspectral image, and for classifying regions within the scene. The method may be used in particular for identifying anomalous (novelty or outlier) features within the scene. In the method, adapted in a preferred embodiment from a known expectation maximisation algorithm, a “measure of outlierness” is determined and used to weight the contribution of training samples for a scene to the component statistics in a statistical model representing features in the scene. Preferably, the measure of outlierness is based upon the ? parameter in a Student's t-distribution and the invention provides techniques for parameterising the ? parameter and other parameters of the model.