The application of spatial domain in optimum initialization for clustering image data using particle swarm optimization

Abstract

Clustering algorithms are affected by the initial seeds, therefore any improvement of the initialization process can improve the final clustering results. There exist several initialization algorithms that most of them are focused on using the distance and density based metrics defined in the feature space. However image space has a great potential to be used as the search space for initial seeds. In this research, developing clustering initialization using spatial information (image space) and spectral information (feature space) with the help of particle swarm optimization has been examined. Standard deviation and homogeneity of pixels in the image space in addition to distance and density of points in the feature space have been utilized in the objective function of the particle swarm optimization. Two different search spaces (feature and image spaces) and 26 objective functions have been applied to a simulated image and two real satellite multi-spectral images. Comparing the results of 26 cases with four prevailing initialization methods, demonstrated that searching for initial seeds in the image space using PSO with a full objective function (using four spectral–spatial criteria) can produce better results than the other tested cases. Using this case for k-means clustering initialization, led to about 20% improvement in overall accuracy relative to the clustering results with commonly used initialization algorithms. © 2020 Elsevier Ltd