Color pixel classification using genetic fuzzy system: Case study on earth surface classification

From satellite image, people can see any objects on the earth surface like houses, streets, lands, vegetation, and water. To classify those objects, observers have to be able to distinguish objects in the image. One of the simplest methods is by analyzing pixel color. In this case, the fuzzy classification system is chosen since there are some overlapping in the pixel color characterized for certain objects. Problem of this system comes when there are no available rules for describing the classification. Therefore, genetic fuzzy system is used for creating the rules. This training process is divided in to two steps which are, learning process to create the group of rules, and tuning process to optimize the fuzzy membership function. The result is measured by CCR (Correct Classification Rate). During the training process, the CCR values are increased after the tuning process is done. The highest CCR value recorded for training process is 82,49%. Value of execution time depends on the number of rules that are used.