Region-based urban road extraction from VHR satellite images using Binary Partition Tree

•Binary Partition Tree provides an effective representation for urban roads in VHR images.•Road region of interest was preliminary obtained for highlighting potential road regions.•Geometrical and structural information were used for modelling road regions.•Fuzzy logic was used to deal with the associated uncertainties.

This paper provides a hierarchical method for urban road extraction. It consists of (1) obtaining the road region of interest from a VHR image, (2) hierarchically representing this road region of interest in a Binary Partition Tree (BPT), and extracting the roads based on this BPT at hierarchical levels. Besides using two existing geometrical features (i.e. compactness and elongation), we define two other structural features based on orientation histograms and morphological profiles to guide the region merging of BPT. The morphological profiles are constructed using a series of path openings, which facilitate modeling linear or curved structures. The proposed method was applied to two types of VHR images with different urban settings, corresponding to a Pléiades-B image of Wuhan, China, and a Quickbird image of Enschede, the Netherlands. Experimental results show that the proposed method was able to group adjacent small segments that have high spectral heterogeneity and low road-like geometrical properties to form more meaningful roads sections, and performed superior to the existing methods. Furthermore, we compared the proposed method with two other existing methods in the literature. We conclude that the proposed method can provide an effective means for extracting roads over densely populated urban areas from VHR satellite images.