A hierarchical Markov random field for road network extraction and its application with optical and SAR data

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper,we propose a hierarchical Markovian framework to extract the road network with optical and synthetic aperture radar (SAR) data. We propose a generalization of a previous method based on a low-level step (features extraction) and a high-level step (use of contextual information). The main novelties of the proposed approach are the use of more general elements to represent road candidates, which simplifies and generalizes the method, the fusion of different sensors during both lower and higher levels and the introduction of a second MRF in a hierarchical way. The approach is tested and evaluated using TerraSAR-X and Quickbird data.

Original languageEnglish
Title of host publication2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings
Pages1159-1162
Number of pages4
DOIs
Publication statusPublished - 16 Nov 2011
Event2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Vancouver, BC, Canada
Duration: 24 Jul 201129 Jul 2011

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011
Country/TerritoryCanada
CityVancouver, BC
Period24/07/1129/07/11

Keywords

  • Data fusion
  • optical data.
  • road network extraction
  • synthetic aperture radar (SAR) data

Fingerprint

Dive into the research topics of 'A hierarchical Markov random field for road network extraction and its application with optical and SAR data'. Together they form a unique fingerprint.

Cite this