The foremost approach to the detection of militarily significant targets in hyperspectral imagery is through the use of anomaly detection processes. These may be applied to imagery in order to identify those pixels that contain materials uncommon in the scene, on the assumption that military targets will match this criterion. The most common approach to anomaly detection for hyperspectral data is through the use of local-area anomaly detection techniques. These extract statistics of the scene in the near-locality of the pixel of interest and then use hypothesis test methods to decide whether the test pixel is anomalous to the training area. Alternative and potentially superior approaches are also available which first attempt to understand the composition of the whole scene in terms of ground cover types. These methods go on to use the extracted scene understanding model to find pixels containing materials that are rare or unseen in the imagery, and mark these as anomalies. This paper compares three anomaly detection approaches, one based on the local area paradigm and two using the scene understanding (global anomaly detection) approach. The latter pair of methods exploit different ways of extracting the scene model. The anomaly detection techniques are examined using real hyperspectral imagery with inserted anomaly pixels. A range of results is presented for different parameterisations of the algorithms. These include anomalous pixel maps at given detection rates and receiver operating characteristic curves.