A data interpretion chain for hyperspectral remote sensing data aimed at basic vegetation mapping applications

In this paper we introduce the first steps towards a comprehensive methodology for hyperspectral data analysis suitable for generic vegetation mapping applications. As hyperspectral data is characterized by the large number of narrow wavebands it is challenging to find an optimal solution for data classification. In case of vegetation mapping is even more complicated as the signatures of vegetation classes are not constant over time and space. In this study we investigate a series of different data processing chains for vegetation interpretation and we introduce a novel methodology to build a multistage, hierarchical data processing approach that is able to combine the advantages of different processing chains already available to the users. The data classification is carried out using a binary decision tree structure where at each node the most useful input source is used and the structure of the tree is created by using the predicted accuracy level of the whole structure estimated from test classification of data subsets.