COLOUR BASED IMAGE SEGMENTATION USING K-MEANS CLUSTERING

Primarily due to the progresses in spatial resolution of satellite imagery, the methods of segment-based image analysis for generating and updating geographical information are becoming more and more important. This work presents a novel image segmentation based on colour features with K-means clustering unsupervised algorithm. In this we did not used any training data. The entire work is divided into two stages. First enhancement of color separation of satellite image using decorrelation stretching is carried out and then theregions are grouped into a set of five classes using K-means lustering algorithm. Using this two step process, it is possible to reduce the computational cost avoiding feature calculation for every pixel in the image. Although the colour is not frequently used for image segmentation, it gives a high discriminative power of regions present in the image.