SAR image road network extraction with scene context priming

Author

Cao, Yongfeng ; Tang, Huang

Author_Institution

Sch. of Math. & Comput. Sci., Guizhou Normal Univ., Guiyang, China

fYear

2011

fDate

24-29 July 2011

Firstpage

1806

Lastpage

1809

Abstract

In this paper, a novel method of road network extraction with scene context priming for Synthetic Aperture Radar (SAR) images is proposed. In this method, the scene contextual information of each site, that this site is in road exist scene or road-not-exist scene, is got by an SVM classifier. Then this information is used for guiding perceptual grouping of the lines and curves got by a line extractor. The output of perceptual grouping is further refined by Markov Random Field (MRF) based global optimization method to get the final road network. The performance of the method is tested on a high resolution TerraSAR-X image of urban scene.

Keywords

Markov processes; geophysical image processing; geophysical techniques; image classification; image resolution; random processes; synthetic aperture radar; Markov random field; SAR image road network extraction method; SVM classifier system; global optimization method; high resolution TerraSAR-X image; road-exist scene; road-not-exist scene; synthetic aperture radar image; Context; Data mining; Feature extraction; Remote sensing; Roads; Support vector machines; Synthetic aperture radar; Road network; SAR images; Scene context;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International

Conference_Location

Vancouver, BC

ISSN

2153-6996

Print_ISBN

978-1-4577-1003-2

Type

conf

DOI

10.1109/IGARSS.2011.6049472

Filename

6049472