Title :
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
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;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International
Conference_Location :
Vancouver, BC
Print_ISBN :
978-1-4577-1003-2
DOI :
10.1109/IGARSS.2011.6049472
