Infrared-image classification using expansion matching filters and hidden Markov trees

Forward-looking infrared (FLIR) images of targets are characterized by the different target components visible in the image, with are dependent on the target-sensor orientation and target history (i.e., which target components are hot). We define a target class as a set of contiguous target-sensor orientations over which the associated image is relatively invariant, or statistically stationary. Given an image from an unknown target, the objective is proper target-class association (target identity and pose). Our principal contribution is an image classifier in which a distinct set of templates is designed for each image class, with templates linked to the object sub-components, and the associated statistics are characterized via a hidden Markov model. In particular, we employ expansion matching (EXM) filters to identify the presence of the target components in the image, and use a hidden Markov tree (HMT) to characterize the statistics of the correlation of the image with the various templates. We achieve a successful classification rate of 92% on a data set of FLIR vehicle images, compared with 72% for a previously developed wavelet-feature-based HMT technique.