A Simulation Study of Some Contextual Classification Methods For Remotely Sensed Data

Various methods for contextual classification of multispectral scanner data have been developed during the last 15 years, aiming at increased accuracy in classified images. The methods have for a large part been of four main types: 1) neighborhood-based classification based on stochastic models for the classes over the scene and for the vectors given the classes; 2) simultaneous classification of all pixels, using, e.g., Markov random-field models; 3) relaxation methods that iteratively modify posterior probabilities using information from an increasing neighborhood; and 4) methods using ordinary noncontextual rules based on transformed data. In the present paper a selection of these methods is presented and compared using computer-gented data on different scenes. Spatial autocorrelation is present in the data. Error rates are compared, and an attempt is made to characterize what kind of errors each particular method makes.