Vehicle Recognition as Changes in Satellite Imagery

Over the last several years, a new probabilistic representation for 3-d volumetric modeling has been developed. The main purpose of the model is to detect deviations from the normal appearance and geometry of the scene, i.e. change detection. In this paper, the model is utilized to characterize changes in the scene as vehicles. In the training stage, a compositional part hierarchy is learned to represent the geometry of Gaussian intensity extrema primitives exhibited by vehicles. In the test stage, the learned compositional model produces vehicle detections. Vehicle recognition performance is measured on low-resolution satellite imagery and detection accuracy is significantly improved over the initial change map given by the 3-d volumetric model. A PCA-based Bayesian recognition algorithm is implemented for comparison, which exhibits worse performance than the proposed method.