Flexible histograms: a multiresolution target discrimination model
In previous work we have developed a methodology for texture recognition and synthesis that estimates and exploits the dependencies across scale that occur within images. In this paper we discuss the application of this technique to synthetic aperture radar (SAR) vehicle classification. Our approach measures characteristic cross-scale dependencies in training imagery; targets are recognized when these characteristic dependencies are detected. We present classification results over a large public database containing SAR images of vehicles. Classification performance is compared to the Wright Patterson baseline classifier. These preliminary experiments indicate that this approach has sufficient discrimination power to perform target detection/classification in SAR.