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Hierarchical hybrid decision tree multiscale fusion for urban image classification

Multi-scale classification is an important tool for urban image classification, because the objects in an urban scene may have very different spatial scales. In technical literature, this idea is usually performed either using the same classifier (ensemble of classifiers) at multiple resolutions, or working on sets of features at multiple scales. Although this may be very efficient approach for peculiar situations, it is not as effective, as it does not adapt to different classes. Accordingly, it can be useful to design a procedure based on the possibility to assemble specialized (multi-scale) classification chains automatically, according to the classes to be recognized and their peculiar scales or features. We present a methodology that combines different processing chains (each one composed by a feature selection and a classification step) and automatically adapts to the properties of the classes available in an urban scene.