The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image-processing tools. Hyperspectral imaging enables the characterization of regions based on their spectral properties which provides a rich amount of information. This paper proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation relying on the binary partition tree (BPT). The BPT is a hierarchical region-based representation having a rather generic construction (to a large extend, application independent). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Based on region-merging techniques, the BPT construction is investigated by studying the hyperspectral region models and the associated similarity metrics. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application dependent techniques on it. The application dependent processing of BPT is generally implemented through a specific pruning of the tree. In this paper, a pruning strategy is proposed and discussed in a classification context. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representation.
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