MODELING OBJECT CLASSES IN AERIAL IMAGES USING HIDDEN
MARKOV MODELS

Shawn Newsam, Sitaram Bhagavathy, and B. S. Manjunath
Dept. of Electrical and Computer Engineering
University of California at Santa Barbara,
Santa Barbara, CA 93106
E-mail: {snewsam, sitaram, manj }@ece.ucsb.edu

Abstract

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 geographic processes and the state transitions correspond
to the spatial arrangement of the processes. 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.