Figure 2: From left to right: Original image, result image with fusion (with monteverdi viewer) of original image and fancy classification and input image with fancy color classification from labeled image.
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@@ -238,9 +240,12 @@ The `Figure 2` represents them after a color mapping by the same LUT.
Figure 4: From left to right: Original image, and fancy colored classified image obtained by a majority voting fusion of the 6 classification maps represented in Fig. 4.13 (water: blue, roads: gray, vegetation: green, buildings with red roofs: red, undecided: white)
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@@ -305,7 +311,8 @@ Shafer*, the fusion of the six input classification maps represented in
fusion gives access to a more precise and robust classification map
Figure 5: From left to right: Original image, and fancy colored classified image obtained by a Dempster Shafer fusion of the 6 classification maps represented in Fig. 4.13 (water: blue, roads: gray, vegetation: green, buildings with red roofs: red, undecided: white).
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@@ -433,7 +440,9 @@ a class index) from an input predictor. The workflow is the same as
classification. First, the regression model is trained, then it can be
used to predict output values. The applications to do that are and .
