Road extraction from SAR multi-aspect data supported by a statistical context-based fusion

K. Hedman, S. Hinz, U. Stilla

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

14 Scopus citations

Abstract

In this paper we describe a fusion approach for automatic object extraction from multi-aspect SAR images. The fusion is carried out by means of the Bayesian probability theory. The first step consists of a line extraction in each image, followed by attribute extraction. Based on these attributes the uncertainty of each line segment is estimated, followed by an iterative fusion of these uncertainties supported by context information and sensor geometry. On the basis of a resulting uncertainty vector each line obtains an estimation of the probability that the line really belongs to a road.

Original languageEnglish
Title of host publication2007 Urban Remote Sensing Joint Event, URS
DOIs
StatePublished - 2007
Event2007 Urban Remote Sensing Joint Event, URS - Paris, France
Duration: 11 Apr 200713 Apr 2007

Publication series

Name2007 Urban Remote Sensing Joint Event, URS

Conference

Conference2007 Urban Remote Sensing Joint Event, URS
Country/TerritoryFrance
CityParis
Period11/04/0713/04/07

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