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

摘要

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.