Markov random field based super-resolution mapping for identification of urban trees in VHR images

Extraction of individual tree crown objects from very high resolution imagery is a challenging task given the limited spectral and spatial resolution of space-borne systems and the complexity of the urban space. Besides, traditional pixel based image classification techniques do not fully exploit the spatial and spectral characteristics of tree crowns imaged in remote sensing datasets. In this work, we propose a contextual and probabilistic detection of tree crowns in very high resolution imagery by using super resolution mapping (SRM) based on Markov random fields (MRF). Our method models and objective energy function which considers the conditional probabilities of panchromatic and multispectral values of a Quickbird image and models the prior information as the spatial smoothness of pixels labeled as tree crown. We apply this method for extraction of tree crown objects in a residential area in the Netherlands. We found that the proposed method leads to improvement in tree crown identification compared with a maximum likelihood classification of a pan-sharpened product.