Satellite image clustering and optimization using K-means and PSO

Particle swarm optimization (PSO) is a population based optimization technique, inspired by social behavior of animal and birds, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a brief overview of the basic concepts of clustering techniques proposed in last four decades and a quick review of different similarity measure has been done. K-means is implemented to cluster satellite image of city Mumbai (India) and standard image such as mandrill and clown in HSV color space. PSO is used to optimize clusters results from k-means and within-cluster sums of point-to-centroid distances are measured. The results illustrate that our approach can produce more compact and optimized clusters than the K means alone.