Modeling object classes in aerial images using hidden Markov models

We propose a canonical model 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 geographic processes and the state transitions correspond to the spatial arrangement of the processes. A novel onedimensional approach reduces the computational complexity. We show that the model is effective in characterizing objects of interest in spatial datasets, in terms of their underlying texture motifs. We also demonstrate the potential of the model for identifying the classes of unknown objects.