TY  - JOUR

JO  - Geoscience and Remote Sensing Letters, IEEE

TI  - Improving Urban Road Extraction in High-Resolution
Images Exploiting Directional Filtering, Perceptual
Grouping, and Simple Topological Concepts

IS  - 3

SN  - 1545-598X  

SP  -  387

EP  -  391

AU  - Gamba, P.

AU  - Dell'Acqua, F.

AU  - Lisini, G.

PY  - 2006

KW  - Perceptual grouping

KW  - street extraction

KW  - urban remote sensing

KW  - Perceptual grouping

KW  - street extraction

KW  - urban remote sensing

VL  - 4

JA  - Geoscience and Remote Sensing Letters, IEEE

AB  -  In this letter, the problem of detecting urban road
networks from high-resolution optical/synthetic aperture
radar (SAR) images is addressed. To this end, this letter
exploits a priori knowledge about road direction
distribution in urban areas. In particular, this letter
presents an adaptive filtering procedure able to capture the
predominant directions of these roads and enhance the
extraction results. After road element extraction, to both
discard redundant segments and avoid gaps, a special
perceptual grouping algorithm is devised, exploiting
colinearity as well as proximity concepts. Finally, the road
network topology is considered, checking for road
intersections and regularizing the overall patterns using
these focal points. The proposed procedure was tested on a
pair of very high resolution images, one from an optical
sensor and one from a SAR sensor. The experiments show an
increase in both the completeness and the quality indexes
for the extracted road network.

ER  - 