&lt;title&gt;Combination of fuzzy sets and Dempster-Shafer theories in forest map updating using multispectral data&lt;/title&gt;

Leduc, F.; Solaiman, Basel; Cavayas, François

doi:10.1117/12.421120

Cited by 4 publications

(2 citation statements)

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“…Another approach for transforming membership values into mass functions was proposed in [15], [16] that considers nested focal elements. This method can be illustrated with figure 2.…”

Section: From Membership To Basic Belief Valuesmentioning

confidence: 99%

Comparison of several fusion paradigms applied to pixel-based image classification

Leduc¹

2006

2006 9th International Conference on Information Fusion

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This paper is concerned with the development of FuRII, a pixel-based image classification tool developed at DRDC Valcartier. FuRII is based on fuzzy sets and evidence theories and is implemented as an ENVI toolbox. The aim with this tool is to compare several fusion operators and rules in the context of image classification applied to land cover mapping. Several fuzzy fusion operators (conjunctive, disjunctive, adaptive and quantified adaptive fusion) and evidential fusion rules (Dempster, Dubois and Prade, Yager and Smets) are tested. FuRII permits to model imprecise knowledge with membership functions and fusion can be performed directly with membership values or with mass functions. In this later case, a transformation of membership values into basic belief values is computed. Finally, FuRII permits integration of source reliability into the fusion process.This paper is arranged as follow. Section 2 gives some theoretical background while section 3 contains a short description of the parameters that can be controlled within FuRII. Section 4 gives a description of the data sets used in this study. Section 5 presents the results obtained with different configurations. Finally section 6 discusses the results and section 7 concludes this document. Theoretical background 2.1 Fuzzy setsFuzzy sets theory was proposed by Zadeh in 1965 [1] in order to deal with imprecise information. The fuzzy inference process is the comparison of an observation (a fact) that can be crisp or fuzzy with imprecise information represented by a membership function. The result is a membership value that measures to what extent the fact corresponds to a class according to the feature modeled with the membership function. When considering M features (i.e. spectral bands) and N classes, the fuzzy inference produces a matrix of M by N membership values. In order to decide which class the object belongs to, fusion operators are necessary.

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“…Another approach for transforming membership values into mass functions was proposed in [15], [16] that considers nested focal elements. This method can be illustrated with figure 2.…”

Section: From Membership To Basic Belief Valuesmentioning

confidence: 99%

Comparison of several fusion paradigms applied to pixel-based image classification

Leduc¹

2006

2006 9th International Conference on Information Fusion

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“…In most studies, the thematic application of DempsterShafer theory concems land use and land cover mapping, sometimes, by considering temporal changes (Leduc et al, 2001). …”

Section: G Mercier B Solaimanmentioning

confidence: 99%

Land use and land cover change prediction with the theory of evidence: a case study in an intensive agricultural region of France

Hubert‐Moy¹,

Corgne²,

Mercier³

et al.

Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)

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In intensive agricultural regions, accurate assessment of the spatial and temporal variation of winter vegetation covering is a key indicator of water transfer processes, essential for controlling land management and helping local decision making. Spatial prediction modeling of winter bare soils is complex and it is necessary to introduce uncertainty in modeling land use and cover changes, especially as high spatial and temporal variability are encountered. Dempster 's fusion rule is used in the present study to spatially predict the location of winter bare fields for the next season on a watershed located in an intensive agricultural region. It expresses the model as a function of past-observed bare soils, field size, dislance ji-om farm buildings, agroenvironmental action, and production quotas per ha. The model well predicted the presence of bare soils on 4/5 of the total area. Eke spatial distribution of misrepresented fields is a good indicator for identifiing change factors

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Propagating aleatory and epistemic uncertainty in land cover change prediction process

Ferchichi

Boulila

Farah

2017

Ecological Informatics

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An objective of satellite remote sensing is to predict or characterize the land cover change (LCC) over time. Sometimes we are capable of describing the changes of land cover with a probability distribution. However, we need sufficient knowledge about the natural variability of these changes, which is not always possible. In general, uncertainties can be subdivided into aleatory and epistemic. The main problem is that classical probability theory does not make a clear distinction between aleatory and epistemic uncertainties in the way they are represented, i.e., both of them are described with a probability distribution. The aim of this paper is to propagate the aleatory and epistemic uncertainty associated with both input parameters (features extracted from satellite image object) and model structure of LCC prediction process using belief function theory. This will help reducing in a significant way the uncertainty about future changes of land cover. In this study, the changes prediction of land cover in Cairo region, Egypt for next 16 years (2030) is anticipated using multi-temporal Landsat TM5 satellite images in 1987 and 2014. The LCC prediction model results indicated that 15% of the agriculture and 6.5% of the desert will be urbanized in 2030. We conclude that our method based on belief function theory has a potential to reduce uncertainty and improve the prediction accuracy and is applicable in LCC analysis.

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<title>Combination of fuzzy sets and Dempster-Shafer theories in forest map updating using multispectral data</title>

Cited by 4 publications

References 5 publications

Comparison of several fusion paradigms applied to pixel-based image classification

Comparison of several fusion paradigms applied to pixel-based image classification

Land use and land cover change prediction with the theory of evidence: a case study in an intensive agricultural region of France

Propagating aleatory and epistemic uncertainty in land cover change prediction process

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