Modeling and Clustering Techniques for Multi-Band Change Detection

In this paper, two unsupervised methods for multi-band change detection are presented. Both methods model the multi-band difference image histogram in order to characterize different degrees of observed spectral changes. In the first approach, we extend the single-band change detection algorithm proposed by Prieto and Bruzzone in which a two-component mixture density is fit to the observed difference image histogram, where the components correspond to the changed and unchanged populations. The second approach employs the hierarchical modal associative clustering algorithm proposed by Li et al., in which a hierarchy of kernel densities at different bandwidths is employed to model the multi-band difference image histogram. The kernel density modes correspond to different scales of changes and are analyzed with respect to increasing kernel bandwidth so that changes occurring at different scales may be identified. Experiments, carried out on ASTER data; are conducted to display the changes captured by each method as well as to illustrate how the degrees of detected changes can be interpreted with respect to model complexity or scale.