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New hyperspectral data representation using binary partition tree
Valero Valbuena, Silvia; Salembier Clairon, Philippe Jean; Chanussot, Jocelyn
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. This paper introduces a new hierarchical structure representation for such images using binary partition trees (BPT). Based on region merging techniques using statistical measures, this region-based representation reduces the number of elementary primitives and allows a more robust filtering, segmentation, classification or information retrieval. To demonstrate BPT capabilites, we first discuss the construction of BPT in the specific framework of hyperspectral data. We then propose a pruning strategy in order to perform a classification. Labelling each BPT node with SVM classifiers outputs, a pruning decision based on an impurity measure is addressed. Experimental results on two different hyperspectral data sets have demonstrated the good performances of a BPT-based representation
Award-winning
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
Binary Partition Tree
Support vector machines
Image processing
Signal theory (Telecommunication)
Senyal, Teoria del (Telecomunicació)
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