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

Content Provider	IEEE Xplore Digital Library
Author	Perciano, T. Tupin, F. Hirata, R. Cesar, R.M.
Copyright Year	2011
Description	Author affiliation: Institut Télécom; Télécom ParisTech, TSI Department, 46 Rue Barrault, 75013, France (Perciano, T.; Tupin, F.) \|\| Universidade de São Paulo, Instituto de Matemática e Estatística, 05508-090, Brazil (Hirata, R.; Cesar, R.M.)
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.
Starting Page	1159
Ending Page	1162
File Size	662293
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049403
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Roads Laser radar Optical sensors Optical imaging Feature extraction Radar imaging optical data. Data fusion road network extraction synthetic aperture radar (SAR) data
Content Type	Text
Resource Type	Article

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