Development of Swarm Based Hybrid Algorithm for Identification of Natural Terrain Features

Abstract

Swarm Intelligence techniques facilitate the configuration and collimation of the remarkable ability of a group members to reason and learn in an environment of uncertainty and imprecision from their peers by sharing information. This paper introduces a novel hybrid approach PSO-BBO that is tailored to perform classification. Biogeography-based optimization (BBO) is a recently developed heuristic algorithm, which proves to be a strong entrant in this area with the encouraging and consistent performance. But, as BBO lacks inbuilt property of clustering, it is hybridized with Particle Swarm Optimization (PSO), which is considered as a good clustering technique. We have successfully applied this hybrid algorithm for classifying diversified land cover areas in a multispectral remote sensing satellite image. The results illustrate that the proposed approach is very efficient and highly accurate land cover features can be extracted by using this method. Also, this technique can easily be extended for other global optimization problems.