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Feature fusion to improve road network extraction in high-resolution SAR images

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4 Author(s)
Lisini, G. ; Dipt. di Elettronica, Pavia Univ., Italy ; Tison, C. ; Tupin, F. ; Gamba, P.

This letter aims at the extraction of roads and road networks from high-resolution synthetic aperture radar data. Classical methods based on line detection do not use all the information available; indeed, in high-resolution data, roads are large enough to be considered as regions and can be characterized also by their statistics. This property can be used in a classification scheme. Therefore, this letter presents a road extraction method which is based on the fusion of classification (statistical information) and line detection (structural information). This fusion is done at the feature level, which helps to improve both the level of likelihood and the number of the extracted roads. The proposed approach is tested with two classification methods and one line extractor. Results on two different datasets are discussed.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:3 ,  Issue: 2 )

Date of Publication:

April 2006

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