Multisource classification using ICM and Dempster-Shafer theory

Abstract

We propose to use evidential reasoning in order to relax Bayesian decisions given by a Markovian classification algorithm (ICM). The Dempster-Shafer rule of combination enables us to fuse decisions in a local spatial neighborhood which we further extend to be multisource. This approach enables us to more directly fuse information. Application to the classification of very noisy images produces interesting results. ).M. Germain and J.-M. Boucher are with the École Nationale Supérieure des Télécommunications de Bretagne, Brest, France.Publisher Item Identifier S 0018-9456(02)04315-2.

Multisensor triplet Markov fields and theory of evidence

“…Although DS fusion is now well known and widely used in various situations [5,9,11,19,[32][33][34]38], its use in the Markov field context is very rare; only a few papers deal with this kind of models [3,6,10,15,31,36]. To be more precise, we show how the use of TMF allows one to simultaneously integrate the possibly evidential aspects of Image and Vision Computing 24 (2006) the prior information and the possibly evidential aspects of the sensors.…”

New trends in Markov models and related learning to restore data

“…It is especially interesting in the case of numerous sensors fusion, when they are of different nature. The use of the DSTE in the context of Markov models is quite recent and is undoubtedly very promising [35,62]. A link with TMC [57] and TMF [59] has also been been established.…”

Land cover classification with multi-source data using evidential reasoning approach

“…The ER method, built on Dempster-Shafer theory of evidence, is a method of inexact reasoning (Shafer, 1967;Giarratano and Riley, 1998). The method has the ability to incorporate all four types of geospatial data, so it has been widely used in a variety of classification tasks (Le Hegarat-Mascle et al, 2000;Foucher et al, 2002;Cohen and Shoshany, 2005;Al Momani et al, 2006;Sun et al, 2008). Additionally, the method can produce interpretive measures (such as degree of support, plausibility and uncertainty) which are useful to classification (Lein, 2003;Cohen and Shoshany, 2005).…”