A two-level Markov random field for road network extraction and its application with optical,SAR, and multitemporal data |
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| Authors: | T. Perciano F. Tupin R. Hirata Jr. R. M. Cesar Jr. |
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| Affiliation: | 1. Signal and Image Processing Department, Télécom ParisTech, LTCI, Paris, France;2. Department of Computer Science, Institute of Mathematics and Statistics, University of S?o Paulo, SP, Braziltperciano@lbl.gov;4. Department of Computer Science, Institute of Mathematics and Statistics, University of S?o Paulo, SP, Brazil |
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| Abstract: | This article introduces a method for road network extraction from satellite images. The proposed approach covers a new fusion method (using data from multiple sources) and a new Markov random field (MRF) defined on connected components along with a multilevel application (two-level MRF). Our method allows the detection of roads with different characteristics and decreases by around 30% the size of the used graph model. Results for synthetic aperture radar (SAR) images and optical images obtained using the TerraSAR-X and Quickbird sensors, respectively, are presented demonstrating the improvement brought by the proposed approach. In a second part, an analysis of different types of data fusion combining optical/radar images, radar/radar images, and multitemporal SAR (TerraSAR-X and COSMO-SkyMed) images is described. The qualitative and quantitative results show that the fusion approach improves considerably the results of the road network extraction. |
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