Arbre de partition binaire: Un nouvel outil pour la représentation hiérarchique et l’analyse des images hyperspectrales

Abstract

This problem discusses here is the hierarchical representation and processing of the hyperspectral imaging. In this framework, Binary Partition Trees (BPTs) are proposed as new hierarchical region-based representation. Based on region merging techniques, the work presented here proposes a strategy for merging hyperspectral regions using a new association measure depending on canonical correlations relating principal coordinates. Once is BPT constructed, this representation can be used for many applications including ltering, segmentation and classi cation.To demonstrate an example of BPT usefulness, a pruning strategy aiming at object detection is discussed. Experimental results demonstrate the good performances of BPT.