Feature Extraction through Information Sharing in Swarm Intelligence Techniques

Abstract

Swarm Intelligence (SI) refers to a kind of problem-solving ability that emerges by the interaction of simple information-processing units. The overall behaviour of the system results from the interactions of individuals through information sharing with each other and with their environment, i.e., the self-organized group behaviour. The chapter details the theoretical aspects and the mathematical framework of the concept of information sharing in each of the swarm intelligence techniques of Biogeography-Based Optimization (BBO), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Bee Colony Optimization (BCO), which are the major constituents of the SI techniques that have been used for land cover feature extraction of multi-spectral satellite images. The authors then demonstrate the results of classification after applying each of the above SI techniques presented in the chapter and calculate the classification accuracy for each in terms of the kappa coefficient generated from the error matrix obtained. For verification, they test their results on two datasets and also calculate the producer’s and the user’s accuracy separately for each land cover feature in order to explore the performance of the technique on different features of the satellite image. From the results, they conclude that the concepts of information sharing can be successfully adapted for the design of efficient algorithms that can be successfully applied for feature extraction of satellite images.