Fusion of Classifications

Brief Description

Fuses several classifications maps of the same image on the basis of class labels.

Tags

Learning, Image Analysis

Long Description

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.

Parameters

Limitations

None

Authors

OTB-Team

See also

ImageClassifier application

Example of use