%0 Journal Article %A Chih-Hung Wu and Chih-Chin Lai and Chun-Yen Chen and Yan-He Chen %D 2010 %J International Journal of Electrical and Computer Engineering %B World Academy of Science, Engineering and Technology %I Open Science Index 41, 2010 %T One-Class Support Vector Machines for Aerial Images Segmentation %U https://publications.waset.org/pdf/690 %V 41 %X Interpretation of aerial images is an important task in various applications. Image segmentation can be viewed as the essential step for extracting information from aerial images. Among many developed segmentation methods, the technique of clustering has been extensively investigated and used. However, determining the number of clusters in an image is inherently a difficult problem, especially when a priori information on the aerial image is unavailable. This study proposes a support vector machine approach for clustering aerial images. Three cluster validity indices, distance-based index, Davies-Bouldin index, and Xie-Beni index, are utilized as quantitative measures of the quality of clustering results. Comparisons on the effectiveness of these indices and various parameters settings on the proposed methods are conducted. Experimental results are provided to illustrate the feasibility of the proposed approach. %P 859 - 864