EuroSDR Projects - Host Ireland

Appendix 8: Documentation by Zhang and Couloigner
Note: we have edited the documentation by changing the references to the literature according to our
nomenclature and by deleting copied parts.
Algorithm
The main steps of our methodology and their corresponding algorithms are briefly described below.
1) Image resampling (only for Aerial and ADS40)
The original images, are resized to half (Aerial) / one fourth (ADS40) of the original size using an
image viewer free-ware.
2) Image segmentation
The K-means clustering algorithm is used for this purpose.
Aerial and ADS40: All the three bands are used. In all the cases, the number of clusters is set to six
and two of them have been identified as possible roads. These two clusters are used to create a road
cluster.
Ikonos: All the four bands are used for the Ikonos1_sub1 image, while only the RGB bands are used
for the Ikonos3_sub1 and Ikonos3_2 images. The NIR band is discarded for the latter two images
because of its low quality. We used the 11-bits images. In all the cases, the number of clusters is set to
six and two of them have been identified as possible roads. These two clusters are used to create a
road cluster.
3) Road cluster refinement
The road cluster is refined by removing the big open areas, which are usually spectrally similar land
covers such as buildings, parking lots, crop fields, and so on. The refinement is based on our shape
descriptors of the Angular Texture Signature in combination of a fuzzy classifier. Details can be found
in (Zhang and Couloigner, 2005a, 2005b).
4) Road centerline segment detection
The road centerline segments are detected from the refined road pixels by a localized and iterative
Radon transform. A gliding window algorithm achieves the localization of the Radon transform. The
window size used is 31 by 31 pixels. The iterative Radon transform is then performed locally on each
window. We have improved the peak selection techniques so that we can accurately estimate the line
parameters of thick lines in the Radon transform. Details can be found in (Zhang and Couloigner
2005c, 2005d).
5) Perceptual grouping
The road segments are further grouped to form the road network. A gap will be bridged if it is shorter
than 5 pixels along the road direction. Possible road intersections are detected and used in the road
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