Hybrid algorithm of Cuckoo Search and Particle Swarm Optimization for natural terrain feature extraction

Abstract

Swarm intelligence is a global research area to improve the optimization of various soft computing and nature inspired techniques. In this study, we have applied the hybrid algorithm of Cuckoo Search (CS) and Particle Swarm Optimization (PSO) for remote sensing image classification of natural terrain features. Remote sensing is the method of acquiring, processing and interpreting the satellite images and related geo-spatial data without any physical contact of that region. The main advantage of using the hybrid concept is that the search strategy used in CS for finding the best host nest for cuckoo egg is resolved by the best position of PSO concept. By using this proposed algorithm, it becomes easier to classify the terrain features and obtained results shows the higher efficiency and greater kappa coefficient value as compare to other swarm intelligence techniques. We have successfully applied the hybridization of Cuckoo Search (CS) and Particle Swarm Optimization (PSO) for classifying diversified land cover areas in a remote sensing satellite image.