Sparse Hierarchical Clustering for VHR Image Change Detection

The traditional clustering approaches are limited for the unsupervised change detection of very high resolution images due to the multimodal distribution of change features. To overcome this difficulty, a sparse hierarchical clustering approach is proposed. Discriminative change features are generated by stacking bitemporal multiscale center-symmetric local binary pattern features. In order to explore the multimodal and hierarchical distribution of the change features, a tree-structured dictionary is learned from the pseudotraining set and the unlabeled data. The sparse reconstruction error, a more robust distance compared to the Euclidean distance, is used to determine the label of each change feature. Comparative experiments demonstrate the effectiveness of the proposed method.