, Experiments on a real satellite image The computation of the complete scene at Figure 3.11-(a) took 4 hours and 21 minutes and 36 seconds

I. , 83 5.2 Overview on combination rules of dependent belief functions, p.84

C. , 85 5.3.1 Reminders and notations 85 5.3.2 Copulas as conjunctive aggregation functions

D. and C. , 88 5.4.1 Random sets and Conjunctive combination of dependent consonant belief functions using copula, p.88

.. , Credal dependent fusion: disjunctive aggregation rule, p.90

C. , , p.91

E. , 93 5.7.1 Consonant mass functions estimation, p.94

C. , , p.100

E. , Conclusion 3.9 Computation time depending on the feature space dimension. SOM-based approach is more appropriated for processing large amount of data than, p.56

C. , False color composite of the SPOT image, p.57

, Classification results in 2 ? with decision by maximum of pignistic probability over all simples hypotheses: (a) SOM-based BBA. (b) DC results, p.58

.. , Classification results in 2 ? with decision by maximum of pignistic probability over all simples hypotheses and all disjunctions of hypotheses: (a) SOM-based BBA. (b) DC results, p.59

E. , Classification results in 2 ? with decision by maximum of pignistic probability: (a) SOM-based approach, p.60

.. , Classification results in D ? with decision by maximum of generalized pignistic probability over all simples hypotheses and all conjunctions of hypotheses: (a) SOM-based approach. (b) DC results, p.61

.. , Credal classification results in D ? through the SOM-based approach: maximum of generalized pignistic probability, p.63

.. , Data fusion levels (a) Low-level fusion or pixel fusion (b) Attributes fusion (c) High-level fusion or decision fusion, p.67

.. , A first coarse joint classification is performed, yielding C MS?? SAR which guarantees class homogeneity between the 2 sensors Kohonen's map is trained from optical multispectral data I MS to yield SOM MS , and then from parameters extracted from the radar data with an enslaved constraint on the location of the neurons of SOM ? SAR |MS . From Kohonen's maps, BBA is performed and then specific discounting operators are applied on the mass depending on their reliability (that yields m ? MS and m ? SAR ). Fusion is performed by PCR6 rule and decision-making is ensured with the maximum of Pignistic probability to yield a joint land cover classification, p.70

.. , radar (b) images acquired over Beauce, France, used in the experiments, Multispectal, p.75

.. , I ? SAR image corresponding to the SAR texture information with false color composition: RGB=(µ, ?, ? 1 ), p.76

.. , Unsupervised K-means classification results (with K = 5 classes) applied jointly on multispectral and SAR information, p.77

.. , The map (a) has been trained with the optical data only, while the map (b) has been enslaved to map (a) and trained with radar data. Hence, co-located neurons bring the same ground information between the 2 maps, SOM maps of size 65 × 65 neurons, p.77

.. , Joint classification results with decision by maximum of pignistic probability over all simples hypotheses, p.78

.. , Results for joint classification with simulated cloud cover. The decision is performed by the maximum of pignistic probability over all simples hypotheses, p.79

.. , 8-(b) Correct Classification Rate (CCR) of 72.28 % for masked zone 1 and 75.60% for masked zone 2, p.80

.. , Copulas and t-norms as aggregation functions, p.92

.. , Sample from the seeds training base, with ? = 0.0097, are shown in grey, simulated models with ? = 0.0097 is shown in blue for Frank copula, in red for Clayton copula and Green for Gumbel copula. Best goodness-of-fit is given by Frank copula, p.95

.. , Sample from the seeds training base, with ? = 0.7132, are shown in grey, simulated models with ? = 0.7132 is shown in blue for Frank copula, in red for Clayton copula and Green for Gumbel copula. Best goodness-of-fit is given by Gumbel copula, p.96

R. and .. , , p.97

.. , Classification results for the weak dependent case in seeds base (cf. Figure 5.2). X stands for the " asymmetry coefficient " and Y for the " length of kernel groove " components of the seeds database, p.98

.. , Classification results for the high dependent case in seeds base (cf. Figure 5.3). X stands for the " length of kernel " and Y for the " length of kernel groove " components of the seeds database, p.99

S. Generated, , p.100

. .. La-comparaison, Caractéristiques des bases de données UCI utilisés pour

E. , E. , and P. ,

.. Résultats-de-la-classification-des-données-simulées, , p.13

T. Contributions-of,

.. , Example of some special classes of mass functions where the conditions imposed by their definitions are putted in boldface, p.24

.. , The sequence of Dedekind's numbers, p.35

D. Memory-size-requirements-for, , p.37

.. , Characteristics of the UCI data sets used for comparison, p.54

, Classification results of SOM-based BBA in 2 ? for different value of ?, p.55

.. , Classification results in 2 ? of EVCLUS, ECM and SOM-based BBA with decision by the maximum of pignistic probability, p.55

1. , DST legend used on classification results of Figure 3, p.58

, Quantitative results in 2 ? obtained using the confusion matrix for DC approach, p.59

.. , Quantitative results in 2 ? obtained using the confusion matrix for the proposed SOM-based approach, p.60

1. , Legend used for classification D ? shown in Figure 3, p.62

, Quantitative results in D ? obtained using the confusion matrix for DC approach, p.62

.. , Quantitative results in D ? obtained using the confusion matrix for the proposed SOM-based approach, p.62

S. Haralik-features-computed-from-the and .. , , p.71

.. , Local moments measures computed from the SAR image, p.72

.. , Quantitative results obtained using the confusion matrix, p.79

.. , Means and covariances of generated data sets, p.100

C. and .. , , p.101

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