TY - JOUR
T1 - A two-level Markov random field for road network extraction and its application with optical, SAR, and multitemporal data
AU - Perciano, T.
AU - Tupin, F.
AU - Hirata, R.
AU - Cesar, R. M.
N1 - Publisher Copyright:
© 2016 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2016/8/17
Y1 - 2016/8/17
N2 - 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.
AB - 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.
U2 - 10.1080/01431161.2016.1201227
DO - 10.1080/01431161.2016.1201227
M3 - Article
AN - SCOPUS:84978873022
SN - 0143-1161
VL - 37
SP - 3584
EP - 3610
JO - International Journal of Remote Sensing
JF - International Journal of Remote Sensing
IS - 16
ER -