A two-level Markov random field for road network extraction and its application with optical, SAR, and multitemporal data

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...

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Bibliographic Details
Published in:International journal of remote sensing 2016-08, Vol.37 (16), p.3584-3610
Main Authors: Perciano, T., Tupin, F., Hirata Jr, R., Cesar Jr, R. M.
Format: Article
Language:English
Subjects:
Computer Science
connected components
data fusion
Extraction
Fields (mathematics)
Image Processing
Markov processes
Markov Random Fields (MRFs)
Multilevel
multitemporal data
optical data
OTHER INSTRUMENTATION
Radar imagery
Road detection
Roads
SAR data
Satellite imagery
Synthetic aperture radar
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Summary: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.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2016.1201227