A Comparative Study of Clustering Methods for Urban Areas Segmentation from High Resolution Remote Sensing Image

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.