Segmentation of digital image plays a major role in computer visualization. It is used to extract meaningful objects that exist on the images. Region based clustering is done to extract objects based on the colors present in the satellite images. The principle of clustering is to identify the similar domains from a huge data set to produce an accurate representation of the image. In this paper, k-means, fuzzy c means and kernel fuzzy c means clustering algorithms are used to partition an image data set into number clusters. The images are clustered into four and six categories for which the qualities of the images are compared through the internal criterion techniques Davies-Bouldin index and Dunn index. For this paper, experiment is carried out with more than 100 satellite images. Finally the PASCO Satellite Ortho (PSO) satellite image is selected, which covers the areas around Mt. Kaimondake in Kagoshima, Japan. The experimental results reveal that the quality of the clustered partitions based on the internal criterion conclude, kernel fuzzy c means clustering algorithm performs better than fuzzy c means and k-means clustering methods. KeywordsSatellite image segmentation, kernel fuzzy clustering, kmeans clustering, image quality, Davies-Bouldin index, dunn index internal criterion.