A Successive Decision Tree Approach to Mining Remotely Sensed Image Data

Abstract

Decision trees (DT) has been widely used for training and classification of remotely sensed image data due to its capability to generate human interpretable decision rules and its relatively fast speed in training and classification. This chapter proposes a successive decision tree (SDT) approach where the samples in the ill-classified branches of a previous resulting decision tree are used to construct a successive decision tree. The decision trees are chained together through pointers and used for classification. SDT aims at constructing more interpretable decision trees while attempting to improve classification accuracies. The proposed approach is applied to two real remotely sensed image datasets for evaluations in terms of classification accuracy and interpretability of the resulting decision rules.