An Analytical Study of the Remote Sensing Image Classification Using Swarm Intelligence Techniques
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 6
Abstract
Swarm intelligence is one of the hot nature inspired techniques that is being used in the many areas of research to optimization the solution of computational problems. In this paper, we have considered swarm intelligence based techniques for remote sensing image classification. Remotely Sensed images are the images captured by satellite. The swarm intelligence based techniques considered for the analytical study of the remote sensing image classification are Particle Swarm Optimization (PSO), Artificial Bee Colony Optimization (ABC), Cuckoo Search (CS), Biogeography Based Optimization (BBO), hybrid ACO/BBO, hybrid CS/PSO, hybrid CS/ACO and hybrid ABC/BBO. The accuracy assessment parameter taken for the review results are kappa coefficient, user’s accuracy, producer’s accuracy and overall accuracy. The review is being done on the dataset of Alwar, Rajasthan. The main goal of this paper is to present an analytical review on the existing swarm intelligence based techniques for the classification of remote sensing images.
Authors and Affiliations
Shelja Singla, Priyanka Jarial, Gaurav Mittal
Keywords
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