An advanced classification system based on the back-propagation of consensus

Abstract

In this paper, we present an advanced classification system aimed at obtaining an accurate and reliable supervised classification of remote-sensing images. The proposed system is based on a closed-loop architecture composed of an ensemble of classifiers, which are combined and integrated within a framework based on Markov Random Fields (MRFs). The basic ideas considered in the definition of the system are: i) to jointly exploit the effectiveness of the aforementioned methodologies to increase the classification accuracy with respect to standard classification approaches; ii) to implement an iterative procedure based on the back- propagation of the consensus (obtained from the ensemble of classifiers integrated with the MRF approach) from the output to the input of the system, for increasing the accuracy in the estimation of classifier parameters and hence the effectiveness of the system. I. INTRODUCTION Automatic classification is one of the most important and studied topics in the field of the analysis of remote sensing images. In the literature several approaches have been proposed for the supervised classification of remote-sensing data. These approaches range from standard statistical methods to advanced neural-network techniques, from knowledge- based methodologies to fuzzy algorithms, and from multiple classifier systems to contextual techniques. Although many of these approaches revealed effective in different application domains, often they do not exhibit accuracies sufficient for meeting end-user requirements in complex classification problems. Consequently, it is necessary to develop novel and advanced classification approaches capable to further increase accuracies in automatic classification of remote-sensing images. In this paper, we present an advanced classification system aimed at obtaining an accurate and reliable supervised classification of remote-sensing data. The proposed system is based on a closed-loop architecture, composed of an ensemble of classifiers, which are combined and integrated with a Markov Random Field (MRF) approach and with a recursive procedure of back-propagation of the consensus obtained in output of the system. II. THE PROPOSED CLASSIFICATION SYSTEM