Combining Hyperspectral Data Processing Chains for Robust Mapping Using Hierarchical Trees and Class Memberships

In this letter, we introduce a methodology to combine decisions of multiple hyperspectral data processing chains using an already tested preselection step and a novel algorithm for the data labeling procedure. More specifically, we exploit a hierarchical binary decision tree (HBDT) optimization algorithm to select the most suitable processing chains for a given mapping problem. Then, a new methodology for decision fusion is introduced, based on weighting the class probability membership values. Experimental results in two test areas show great potentials for the novel procedure, identified as particularly useful for generic mapping of complex environments due to its flexibility and robustness. Moreover, accuracy values are improved with respect to those obtained by HBDT alone.