BIG BANG- BIG CRUNCH BASED SATELLITE IMAGE CLASSIFICATION

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Published: Oct 20, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i8.4615
Keywords:
Big-Bang Big Crunch, Biogeography Based Optimization (BBO), Particle Swarm Optimization (PSO), Satellite Image Classification

Main Article Content

Sunita Sharma
Vinod Kumar Panchal

Abstract

Nature has always been a major source of inspiration to researchers. Nowadays researchers are working on the algorithms that are inspired by the nature. A number of algorithms are proposed which are based on inspiration from nature like Bio Geography based Optimization (BBO), Particle Swarm Optimization (PSO) etc. Recently a new nature inspired algorithm – Big Bang- Big Crunch (BBBC) has been proposed that relies on one of the theories of the evolution of the universe. In the Big Bang phase, particles are randomly spread into universe and in Big Crunch phase, randomly distributed particles are converged to a single point. Satellite image classification is an important task because it is the only way we can know about the land cover map of the inaccessible areas. Although a number of algorithms are proposed for satellite image classification, but there is always a search for alternative strategies which could be best suited for a particular land cover feature extraction task in hand. This paper is focused on classification of satellite image of a particular land cover using the theory of Big Bang- Big Crunch. The original BBBC optimization algorithm does not have the inbuilt property of classification which is required during image classification. Hence original algorithm has been adapted to classify the satellite image of a particular land cover. The results indicate that highly accurate land cover features can be extracted effectively when the proposed adaptations are used.

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Vol. 8 No. 8 (2017): September-October
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