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Satellite image classification is one of the well adapted research area in the field of satellite, space science, remote sensing and other terrain based feature applications. Its major application is in the field of robotic path planning and terrain feature extraction which further can be used for the GPS based location tracking and path planning from one place to another. In this research, concepts of nature inspired meta-heuristic have been studied for the application of satellite image classification. Meta-heuristic algorithms are approximate and usually non-deterministic. From the types of these meta-heuristic approaches, concepts of Biogeography Based Optimization, Artificial Bee Colony Optimization, Intelligent Water Drops Algorithm, Cuckoo Search, Firefly Algorithm, Particle Swarm optimization, Hybrid Ant Colony Optimization & Biogeography Based Optimization, Hybrid Firefly Algorithm & Ant Colony Optimization, Hybrid Cuckoo Search & Intelligent Water Drops Algorithm, Hybrid Cuckoo Search & Ant Colony Optimization and Hybrid Cuckoo Search & Particle Swarm Optimization has been considered. All the methods are analyzed for the dataset of Alwar region, Rajasthan based dataset. They have been analyzed to evaluate the efficiency of concept for the classification of terrain features in the form of satellite image classification. Evaluation of concept has been made with efficiency parameters of kappa coefficient and accuracy of concept.

Analytical Evaluation of Meta-Heuristic Approaches For Satellite Image Classification