Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory

Land cover classification using multispectral satellite images is a very challenging task with numerous practical applications. We propose a multistage classifier that involves fuzzy rule extraction from the training data and then the generation of a possibilistic label vector for each pixel using the fuzzy rule base. To exploit the spatial correlation of land cover types, we propose four different information aggregation methods which use the possibilistic class label of a pixel and those of its eight spatial neighbors for making the final classification decision. Three of the aggregation methods use the Dempster-Shafer theory of evidence, while the remaining one is modeled after the fuzzy k-NN rule. The proposed methods are tested with two benchmark seven-channel satellite images, and the results are found to be quite satisfactory. They are also compared with a Markov random field, model-based contextual classification method and found to perform consistently better.