A generalized fuzzy Markov chain-based model for classification of remote-sensing multitemporal images

Abstract

In this work we propose a conceptual generalization of the cascade classification, fuzzy Markov chain-based method introduced in earlier studies. Such generalization, which is based on the assumption of the invertibility of the fuzzy Markov classification model with respect to time, leads to a model that can classify image objects at two points in time simultaneously. We start by defining two temporal modes of operation. In the forward mode a temporal transformation, supported by a transition possibility matrix T, projects an image object’s fuzzy classification for time t into time t + 1 and fuses the updated membership values with the object’s fuzzy classification for time t + 1. In the backward mode the transition matrix is inverted and the fuzzy classification for time t + 1 is updated backwards, i.e. projected into time t. Furthermore, we tackle a key problem with respect to the application of fuzzy Markov chains in remote-sensing data classification: the estimation of transition possibility values. Previously, transition possibilities estimation in the context of fuzzy Markov chain-based multitemporal classification methods has been carried out with the aid of stochastic methods – specifically, through genetic algorithms. In this work we propose an analytical, least squares-based estimation technique, as a more stable and computational efficient alternative to the stochastic approach. Finally, we report on the application of the multitemporal method in the classification of two different test sites – rural and urban – covered by images produced by medium and high resolution orbital, optical sensor systems.

Acknowledgements

This work was supported by CNPq (Brazilian National Council for Scientific and Technological Development), CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior), FAPERJ (Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro), and FINEP (Brazilian Innovation Agency).