Perceptual grouping of 3D features in aerial image using decision tree classifier

We address a new perceptual grouping algorithm for aerial images, which employs a decision tree classifier and hierarchical multilevel grouping strategy in a bottom-up fashion. In our approach, grouping is performed perceptually on 3D features extracted from 2D images, in which the gestalt principles including collinearity, parallelism and L-typed convergence are encoded by the decision tree learning technique. The decision tree is constructed using training samples obtained from the given 3D reference model. Then, each pair of the extracted 3D line features of an input image is classified into one of the learned gestalt primitives. On the other hand, in multilevel grouping procedure, grouping of collated features are performed from lower to higher level, yielding the structured target model. In order to evaluate the proposed algorithm, experiments are carried out on RADIUS model board images. The results show that grouping is performed effectively to extract man-made structures in aerial images.