Multiple Classifier System for Urban Area’s Extraction from High Resolution Remote Sensing Imagery

Abstract

In this paper, a land-cover extraction thematic mapping approach for urban areas from very high resolution aerial images is presented. Recent developments in the field of sensor technology have increased the challenges of interpreting images contents particularly in the case of complex scenes of dense urban areas. The major objective of this study is to improve the quality of land-cover classification. We investigated the use of multiple classifier systems (MCS) based on dynamic classifier selection. The selection scheme consists of an ensemble of weak classifiers, a trainable selector, and a combiner. We also investigated the effect of using Particle Swarm Optimization (PSO) based classifier as the base classifier in the ensemble module, for the classification of such complex problems. A PSO-based classifier discovers the classification rules by simulating the social behaviour of animals. We experimented with the parallel ensemble architecture wherein the feature space is divided randomly among the ensemble and the selector. We report the results of using separate/similar training sets for the ensemble and the selector, and how each case affects the global classification error. The results show that selection improves the combination performance compared to the combination of all classifiers with a higher improvement when using different training set scenarios and also shows the potential of the PSO-based approach for classifying such images.