Mining rules for satellite imagery using evolutionary classification tree

Abstract

Classification Tree (CT) can establish explicit classification rules of Satellite Imagery (SI). However, the accuracy of explicit classification rules are poor. Back-Propagation Networks (BPN) and Support Vector Machine (SVM) both can establish a highly accurate model to predict the classification of SI but cannot generate the explicit rules. This study proposes a novel mining rule method named Evolutionary Classification Tree (ECT) which is composed of Particle Bee Algorithm (PBA) and Classification Tree (CT) that automatically produce self-organized rules to predict the classification of SI. In ECT, CT plays the architecture to represent explicit rules and PBA plays the optimization mechanism to optimize CT to fit the experimental data. 600 experimental data sets were used to compare accuracy and complexity of four model building techniques, CT, BPN, SVM and ECT. The results showed that ECT can produce rules which are more accurate than CT and SVM but less accurate than BPN models. However, BPN is black box models while ECT can produce explicit rules which is an important advantage to mining the explicit rules and knowledge in practical applications.