Classification of Satellite Images Using Partitioned-Feature Based Classifier Model

A classifier model for satellite image data by using Partitioned-Feature based Classifier (PFC)is proposed in this paper. The PFC does not use concatenated feature vectors extracted from the original data at once to classify each datum, but uses extracted feature vectors to classify data separately. In the training stage, the contribution rate calculated from each feature vector group is drawn throughout the accuracy of each feature vector group and then, in the testing stage, the final classification result is obtained by applying weights corresponding to the contribution rate of each feature vector group. The PFC-based classifier is applied to the problem of satellite image classification on a set of image data. The results demonstrate that the PFC-based classifier scheme can optimally enhance the classification accuracy of individual classifiers that use specific feature vector group.