Abstract: A method and apparatus (1) for processing SAR imagery data, comprising: determining variance ratio data from the SAR imagery data; and processing, for use in change detection, the determined variance ratios data by making use of the F-distribution. The method may further comprise selecting a desired false alarm rate; and wherein making use of the F-distribution comprises determining a change detection threshold for the determined variance ratios data that is dependent upon the F-distribution and the desired false alarm rate. Another possibility is that making use of the F-distribution comprises using the F-distribution to determine probabilities for the determined variance ratios data.

Abstract: A method and apparatus (1) for processing SAR imagery data, comprising: determining variance ratio data from the SAR imagery data; and processing, for use in change detection, the determined variance ratios data by making use of the F-distribution. The method may further comprise selecting a desired false alarm rate; and wherein making use of the F-distribution comprises determining a change detection threshold for the determined variance ratios data that is dependent upon the F-distribution and the desired false alarm rate. Another possibility is that making use of the F-distribution comprises using the F-distribution to determine probabilities for the determined variance ratios data.

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.

Abstract: An apparatus and method are provided for classifying elements in an image, in particular elements of a hyperspectral image, where an element is defined by a vector of feature values.