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Road extraction from remote sensing image based on marked point process with a structure mark library

ABSTRACT This paper proposes a road extraction algorithm based on marked point process (MPP) with a structure mark library. Instead of segments as elements in traditional MPP-based road extraction algorithm, the proposed algorithm uses a library including shapes ‘X,’ ‘Y’, ‘L,’ and ‘–’ to build road network. Elements in the library are defined according to the typical structures in the complex road network and their locations are modelled as random points. Based on that modelling, a local structure model to constrain interactions between the elements is built. On the other hand, a spectral measurement model is built by testing the spectral homogeneity and spectral heterogeneity of pixels covered by road body and non-road body. In this way, by using Bayes theorem, all of the models are integrated together into a road extraction model. To extract the optimal road system based on a maximum posterior probability criteria, the simulated annealing RJMCMC (Reversible Jump Markov Chain Monte Carlo) algorithm with related transfer kernels is designed properly. Remote sensing images covering different road networks are experimented with the proposed algorithm and conventional algorithm, respectively. Compared with the results obtained from both of them, it demonstrates that the proposed algorithm is visually and quantitatively effective for road extraction.