EMBEDDING EXPERT KNOWLEDGE TO HYBRID BIO-INSPIRED TECHNIQUES – AN ADAPTIVE STRATEGY YOWARDS FOCUSSED LAND COVER FEATURE EXTRACTION

Abstract

The findings of recent studies are showing strong evidence to the fact that some aspects of biogeography can be adaptively applied to solve specific problems in science and engineering. This paper presents a hybrid biologically inspired technique called the ACO2/PSO/BBO (Ant Colony Optimization2/ Particle Swarm Optimization / Biogeography Based Optimization) Technique that can be adapted according to the database of expert knowledge for a more focussed satellite image classification. The hybrid classifier explores the adaptive nature of Biogeography Based Optimization technique and therefore is flexible enough to classify a particular land cover feature more efficiently than others based on the 7-band image data and hence can be adapted according to the application. The paper also presents a comparative study of the proposed classifier and the other recent soft computing classifiers such as ACO, Hybrid Particle Swarm Optimization – cAntMiner (PSO-ACO2), Hybrid ACO-BBO Classifier, Fuzzy sets, Rough-Fuzzy Tie up and the Semantic Web Based classifiers with the traditional probabilistic classifiers such as the Minimum Distance to Mean Classifier (MDMC) and the Maximum Likelihood Classifier (MLC). The proposed algorithm has been applied to the 7- band cartoset satellite image of size 472 X 576 of the Alwar area in Rajasthan since it contains a variety of land cover features. The algorithm has been verified on water pixels on which it shows the maximum achievable efficiency i.e. 100%. The accuracy of the results have been checked by obtaining the error matrix and KHAT statistics .The results show that highly accurate land cover features can be extracted effectively when the proposed algorithm is applied to the 7-Band Image , with an overall Kappa coefficient of 0.982.