Fuses several classifications maps of the same image on the basis of class labels.
This application allows you to fuse several classification maps and produces a single more robust
classification map. Fusion is done either by mean of Majority Voting, or with the Dempster Shafer
combination method on class labels.
-MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected.
-DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected.
This Belief Function is calculated by mean of the Dempster Shafer combination of Masses of Belief, and
indicates the belief that each input classification map presents for each label value. Moreover, the Masses of
Belief are based on the input confusion matrices of each classification map, either by using the PRECISION
or RECALL rates, or the OVERALL ACCURACY, or the KAPPA coefficient. Thus, each input
classification map needs to be associated with its corresponding input confusion matrix file for the
Dempster Shafer fusion.
-Input pixels with the NODATA label are not handled in the fusion of classification maps. Moreover,
pixels for which all the input classifiers are set to NODATA keep this value in the output fused
image.
-In case of number of votes equality, the UNDECIDED label is attributed to the pixel.
This section describes in details the parameters available for this application. Table 4.120, page 712 presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is FusionOfClassifications.
Parameter key | Parameter type |
Parameter description |
il | Input image list |
Input classifications |
method | Choices |
Fusion method |
method majorityvoting | Choice |
Majority Voting |
method dempstershafer | Choice |
Dempster Shafer combination |
method.dempstershafer.cmfl | Input File name list |
Confusion Matrices |
method.dempstershafer.mob | Choices |
Mass of belief measurement |
method.dempstershafer.mob precision | Choice |
Precision |
method.dempstershafer.mob recall | Choice |
Recall |
method.dempstershafer.mob accuracy | Choice |
Overall Accuracy |
method.dempstershafer.mob kappa | Choice |
Kappa |
nodatalabel | Int |
Label for the NoData class |
undecidedlabel | Int |
Label for the Undecided class |
out | Output image |
The output classification image |
inxml | XML input parameters file |
Load otb application from xml file |
outxml | XML output parameters file |
Save otb application to xml file |
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Input classifications List of input classification maps to fuse. Labels in each classification image must represent the same class.
Fusion method Selection of the fusion method and its parameters. Available choices are:
Label for the NoData class Label for the NoData class. Such input pixels keep their NoData label in the output image and are not handled in the fusion process. By default, ’nodatalabel = 0’.
Label for the Undecided class Label for the Undecided class. Pixels with more than 1 fused class are marked as Undecided. Please note that the Undecided value must be different from existing labels in the input classifications. By default, ’undecidedlabel = 0’.
The output classification image The output classification image resulting from the fusion of the input classification images.
Load otb application from xml file Load otb application from xml file
Save otb application to xml file Save otb application to xml file
To run this example in command-line, use the following:
To run this example from Python, use the following code snippet:
None
This application has been written by OTB-Team.
These additional ressources can be useful for further information: