Application of Modified K-Means Clustering Algorithm for Satellite Image Segmentation based on Color Information

Abstract

The Image segmentation is the process of clustering or partitioning the image into number of sub images based on any one characteristics of the image such as colour, intensity or texture. The segmentation is the one of the middle level important process in the image analysis. The number of segmentation algorithms has been developed for various applications. In the field of satellite image processing, the segmentation is one of the vital step for gathering huge amount of information from the satellite images. The basic k-means clustering algorithm is simple and fast. The main problem associated with this clustering is not producing the same result for every run because the resulting clusters depends on the initial random assignments. In this paper, a modified k-means clustering algorithm is proposed for the effective segmentation of the satellite images. This proposed method always produces the same result for every run. The experimental results proved that the improved k-means algorithm is an efficient and effective method for the satellite image segmentation for the exact and accurate segmentation of satellite images. Keywords: image segmentation; satellite image; kmeans clustering; centroid; fuzzy logic