Hierarchical Hybrid Decision Tree Fusion of Multiple Hyperspectral Data Processing Chains

In many practical applications of hyperspectral remotely sensed data, maps of different land cover classes or features of interest are best achieved by means of different processing algorithms and techniques. In this paper, we introduce a novel methodology to build a multistage hierarchical data processing approach that is able to combine the advantages of different processing chains, which may be best suited for specific classes, or simply already available to the data interpreters. The combination process is carried out using a hierarchical hybrid decision tree architecture where, at each node, the most useful input information source, i.e., the processing chain, is used. The structure of the tree is created by using the predicted accuracy level of the whole structure estimated on a validation set. The final maps are achieved by applying the designed framework to the whole data set. The usefulness of the procedure is proved by two instances of a specific application, i.e., vegetation mapping, in mountainous and plain areas.