The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore, this PhD thesis proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation: the Binary Partition Tree (BPT). This representation can be interpreted as a set of hierarchical regions stored in a tree structure. Based on region-merging techniques, the construction of BPT is investigated in this work by studying 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. Accordingly, some pruning techniques are proposed and discussed according to different applications. This Ph.D is focused in particular on segmentation, object detection and classification of hyperspectral imagery. Experimental results on various hyper spectral data sets demonstrate the interest and the good performances of the BPT representation.