Junction-aware extraction and regularization of urban road networks in high-resolution SAR images

Matteo Negri; Paolo Gamba; Gianni Lisini; Florence Tupin

doi:10.1109/TGRS.2006.877289

Junction-aware extraction and regularization of urban road networks in high-resolution SAR images

Matteo Negri
, Paolo Gamba
, Gianni Lisini
, Florence Tupin

Research output: Contribution to journal › Article › peer-review

Abstract

A general processing framework for urban road network extraction in high-resolution synthetic aperture radar images is proposed. It is based on novel multiscale detection of street candidates, followed by optimization using a Markov random field description of the road network. The latter step, in the path of recent technical literature, is enriched by the inclusion of a priori knowledge about road junctions and the automatic choice of most of the involved parameters. Advantages over existing and previous extraction and optimization procedures are proved by comparison using data from different sensors and locations.

Original language	English
Article number	1704989
Pages (from-to)	2962-2971
Number of pages	10
Journal	IEEE Transactions on Geoscience and Remote Sensing
Volume	44
Issue number	10
DOIs	https://doi.org/10.1109/TGRS.2006.877289
Publication status	Published - 1 Dec 2006

Keywords

High-resolution synthetic aperture radar (SAR)
Markov random fields (MRFs)
Road network
Urban remote sensing

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10.1109/TGRS.2006.877289

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title = "Junction-aware extraction and regularization of urban road networks in high-resolution SAR images",

abstract = "A general processing framework for urban road network extraction in high-resolution synthetic aperture radar images is proposed. It is based on novel multiscale detection of street candidates, followed by optimization using a Markov random field description of the road network. The latter step, in the path of recent technical literature, is enriched by the inclusion of a priori knowledge about road junctions and the automatic choice of most of the involved parameters. Advantages over existing and previous extraction and optimization procedures are proved by comparison using data from different sensors and locations.",

keywords = "High-resolution synthetic aperture radar (SAR), Markov random fields (MRFs), Road network, Urban remote sensing",

author = "Matteo Negri and Paolo Gamba and Gianni Lisini and Florence Tupin",

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AU - Negri, Matteo

AU - Gamba, Paolo

AU - Lisini, Gianni

AU - Tupin, Florence

PY - 2006/12/1

Y1 - 2006/12/1

N2 - A general processing framework for urban road network extraction in high-resolution synthetic aperture radar images is proposed. It is based on novel multiscale detection of street candidates, followed by optimization using a Markov random field description of the road network. The latter step, in the path of recent technical literature, is enriched by the inclusion of a priori knowledge about road junctions and the automatic choice of most of the involved parameters. Advantages over existing and previous extraction and optimization procedures are proved by comparison using data from different sensors and locations.

AB - A general processing framework for urban road network extraction in high-resolution synthetic aperture radar images is proposed. It is based on novel multiscale detection of street candidates, followed by optimization using a Markov random field description of the road network. The latter step, in the path of recent technical literature, is enriched by the inclusion of a priori knowledge about road junctions and the automatic choice of most of the involved parameters. Advantages over existing and previous extraction and optimization procedures are proved by comparison using data from different sensors and locations.

KW - High-resolution synthetic aperture radar (SAR)

KW - Markov random fields (MRFs)

KW - Road network

KW - Urban remote sensing

U2 - 10.1109/TGRS.2006.877289

DO - 10.1109/TGRS.2006.877289

M3 - Article

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JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

IS - 10

M1 - 1704989

ER -

Junction-aware extraction and regularization of urban road networks in high-resolution SAR images

Abstract

Keywords

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