DocumentCode :
2840870
Title :
A Comparative Study of Clustering Methods for Urban Areas Segmentation from High Resolution Remote Sensing Image
Author :
Bedawi, Safaa M. ; Kamel, Mohamed S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada
fYear :
2009
fDate :
Nov. 30 2009-Dec. 2 2009
Firstpage :
169
Lastpage :
174
Abstract :
This paper focuses on evaluating and comparing a number of clustering methods used in color image segmentation of high resolution remote sensing images. Despite the enormous progress in the analysis of remote sensing imagery over the past three decades, there is a lack of guidance on how to select an image segmentation method suitable for the image type and size. Clustering has been widely used as a segmentation approach therefore, choosing an appropriate clustering method is very critical to achieve better results. In this paper we compare five clustering methods that have been suggested for segmentation of images. We focus on segmentation of urban areas in high resolution remote sensing images. Effective clustering extracts regions which correspond to land uses in urban areas. Ground truth images are used to evaluate the performance of clustering methods. The comparison shows that the average accuracy of road extraction is above 75%. The results show the potential of clustering high resolution aerial images starting from the three RGB bands only. The comparison gives some guidance and tradeoffs involved in using each.
Keywords :
geophysical image processing; image colour analysis; image resolution; image segmentation; pattern clustering; remote sensing; clustering methods; color image segmentation; high resolution remote sensing image; road extraction; urban areas segmentation; Building materials; Clustering methods; Data mining; Image resolution; Image segmentation; Layout; Pixel; Remote monitoring; Remote sensing; Urban areas; Aerial images; Affinity propagation; Clustering-based segmentation; Color; K-means; Mean Shift; Remote sensing; Spectral clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on
Conference_Location :
Pisa
Print_ISBN :
978-1-4244-4735-0
Electronic_ISBN :
978-0-7695-3872-3
Type :
conf
DOI :
10.1109/ISDA.2009.109
Filename :
5364761
Link To Document :
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