Modeling object classes in aerial images using hidden Markov models
A canonical model is proposed for object classes in aerial images. This model is motivated by the observation that geographic regions of interest are characterized by collections of texture motifs corresponding to geographic processes. Furthermore, the spatial arrangement of the motifs is an important discriminating characteristic. In our approach, the states of a hidden Markov model (HMM) correspond to the texture motifs and the state transitions correspond to the spatial arrangement of the motifs. A one-dimensional approach reduces the computational complexity. The model is shown to be effective in characterizing objects of interest in spatial datasets in terms of their underlying texture motifs. The potential of the model for identifying the classes of unlabeled objects is demonstrated.