LAND COVER FEATURE EXTRACTION USING HYBIRD SWARM INTELLIGENCE TECHNIQUE – A REMOTE SENSING PERSPECTIVE

Abstract

The findings of recent studies are showing strong evidence to the fact that some aspects of biogeography can be applied to solve specific problems in science and engineering. The proposed work presents a hybrid biologically inspired technique that can be adapted according to the database of expert knowledge for a more focused satellite image classification. The paper also presents a comparative study of our hybrid intelligent classifier with the other recent Soft Computing Classifiers such as ACO, Hybrid Particle Swarm Optimization-cAntMiner (PSO-ACO2), Fuzzy sets, Rough- Fuzzy Tie up and the Semantic Web Based Classifiers and the traditional probabilistic classifiers such as the Minimum Distance to Mean Classifier (MDMC) and the Maximum Likelihood Classifier (MLC).