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Super-resolution mapping for extraction of urban tree crown objects from VHR satellite images

Extraction of individual tree crown objects in urban areas using available VHR space-borne imaging systems remains a challenging image processing task mainly because of two factors: the limited spectral information offered by the sensors and their limited spatial resolution. The spectral information of available VHR satellite sensors is not sufficient to discriminate tree crowns from other land cover classes such as grassland and shrubs using spectral pixel-based classification techniques. This can be solved by using a contextual classification approach, while the limitation of spatial resolution can be reduced by using Super resolution mapping (SRM). In this paper we extend the contextual Markov Random Field (MRF) based SRM method developed earlier for multispectral images to include the panchromatic band with higher spatial resolution. We apply this method for extraction of tree crown objects from Quickbird images, setting the spatial resolution of superresolved map to that of the panchromatic band, 0.6 m. We apply the proposed method for identification of tree crown objects in a residential area in the Netherlands. We find that incorporation of panchromatic band leads to improved object identification. Our object based accuracy assessment indicate that the proposed method identified 73% of the trees in the study area, with some commission errors in tree areas with understory vegetation.