Fusion of image classifications using Bayesian techniques with Markov random fields

Abstract

This study investigates whether combining several different image classifications together with an a priori image model of the expected spatial distribution of the classes can produce a better classification. A maximum likelihood classifier and the cascade-correlation neural network architecture are used to generate various classification maps for satellite image data by varying the input features and network parameter settings. A likelihood for each pixel's class label is derived from the source classifications and combined with a Markov random field spatial image model to produce the final image classification. The method is applied to a ground cover type study based on Landsat Thematic Mapper (TM) imagery. It was found that a carefully selected combination could significantly improve individual classification results.