serves the learning step of both the fusion approaches. The used information for these learning stages are only considered given by one of the three experts (expert 1). The last 12 images (corresponding to 13841 tiles) are used in order to evaluate the classifier fusion methods, considering the information given by the two other experts.

The figure 5 describes the manual segmentation made by one expert and the automatic classification reached by both classifier fusion methods. The dark blue part corresponds to the non considered part of image. First at all if we look on figure 5 the results of the classification of the same image, we note that the sediments are quite well classified. However, just looking this figure 5 we can not say if the classification is good or not, and if one fusion approach is better or not: it remains very subjective. Moreover it could be good for this image and not for others. So we propose to use our measures. Figure 5: Manual segmentation (first) and automatic segmentation given by the probabilistic approach (second) and the distance approach (third).

First we compare the obtained results to the informations given by only the expert 2. The obtained normalized confusion matrix on the test database is given by for the probabilistic approach:

rock cobble ripple sand silt other

0.00 0.01 0.00 0.01 0.**01 99**.97

13.49 24.62 0.00 33.31 0.**00 28**.57

5.37 2.92 47.70 22.33 3.**46 18**.22 8.78 3.10 6.97 59.51 21.**05 0**.59

0.20 0.28 0.99 16.41 82.**10 0**.00

39.45 0 0 0 29.**57 30**.97

and for the distance approach by:

rock cobble ripple sand silt other

0 0.01 99.97 0.01 0.**02 0** 0 32.05 20.74 34.05 13.**16 0** 0 2.90 51.51 9.28 36.**31 0** 0 2.24 4.08 28.93 64.**74 0** 0 0.00 0.14 4.42 95.**44 0** 0 0 30.96 0 69.**03 0**

We note that the distance approach does not classify rock and other. The most of tiles are classified

![0_image_0.png](0_image_0.png)

in ripple and silt and few in sand. The probabilistic approach provides a full confusion matrix. In order to summarize these results, we can give the vector of good-classification rate and the vector of error classification rate given by [0 24.62 47.70 59.51 82.10 30.97]
and [94.30 59.13 54.55 82.84 71.18 148.05] for the probabilistic approach and by [0 32.05 51.51 28.94 95.44 0]
and [50.00 64.03 144.84 72.88 149.43 50.00] for the distance approach. We recall that is not a percentage because of the weights. The vector of good-classification rates can provide a mean of good-classification rate. We obtain here 62.43 for the probabilistic approach and 50.55 for the distance approach. These results tend to prove that the probabilistic approach gives better results than the distance approach. We can also study the difference on homogeneous tiles and inhomogeneous tiles. For instance, for the probabilistic-based approach, the normalized confusion matrix on homogeneous tiles is given by:

|      |
|-------------------------|

|      |
|-------------------------|

rock cobble ripple sand silt other

0 0 0 0 0 100.00

13.49 24.62 0 33.**31 0 28**.58

5.37 2.92 47.70 22.33 3.**46 18**.22 8.78 3.10 6.97 59.51 21.**05 0**.59 0.21 0.28 0.99 16.41 82.**10 0**

0 0 0 0 0 0

and on inhomogeneous tiles:

We observe an important difference. The goodclassification rate is better on the homogeneous tiles
(62.43) than on the inhomogeneous tiles (39.99). Hence

|      |
|-------------------------|

| rock   | cobble   | ripple   | sand   | silt   | other   |
|--------|----------|----------|--------|--------|---------|
| 25.50  | 11.12    | 7.41     | 15.09  | 14.14  | 26.73   |
| 20.97  | 17.84    | 8.32     | 34.02  | 8.15   | 10.71   |
| 13.50  | 5.71     | 30.29    | 29.73  | 7.75   | 13.02   |
| 11.21  | 5.79     | 11.86    | 44.95  | 21.33  | 4.85    |
| 13.79  | 3.24     | 10.24    | 16.40  | 53.55  | 2.77    |
| 39.46  | 0        | 0        | 0      | 29.58  | 30.97   |

|      |
|-------------------------|
