Combining classifiers for robust hyperspectral mapping using Hierarchical Trees and class memberships

In this paper we introduce a methodology to combine decisions of multiple data processing chains using novel algorithms for the selection of the processing chains to be used and also for the data labeling procedure. More specifically we recall how a Hierarchical Binary Decision Tree designing and optimization algorithm can be used to select the most suitable processing chains, given the dataset and the training and validation data. Then, we introduce a new methodology for the decision fusion of these processing chains by using class probability membership values. The test results show great potential of the introduced methodology, identified as particularly useful for generic mapping of vegetation because of its flexibility and robustness. The latter addition improves the already high accuracy level obtained by Hierarchical Binary Decision on the AVIRIS Indian Pine 1992 dataset. While this improvement is not dramatic in terms of overall accuracy, it is shown that the method is more robust in case of classes that are difficult to discriminate using other techniques.