Some empirical localized discriminant analysis methods for classifying images are introduced. They use spatial correlation of images in order to improve classification reducing the 'pseudo-nuisance' present in pixel-wise discriminant analysis. The result is obtained through an empirical (data driven) and local (pixel-wise) choice of the prior class probabilities. Local empirical discriminant analysis is formalized in a framework that focuses on the concept of visibility of a class that is introduced. Numerical experiments are performed on synthetic and real data. In particular, methods are applied to the problem of retrieving the cloud mask from remotely sensed images. In both cases classical and new local discriminant methods are compared to the ICM method.
