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IGARSS
2010

New hyperspectral data representation using binary partition tree

10 years 9 months ago
New hyperspectral data representation using binary partition tree
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
Silvia Valero, Philippe Salembier, Jocelyn Chanuss
Added 05 Mar 2011
Updated 05 Mar 2011
Type Journal
Year 2010
Where IGARSS
Authors Silvia Valero, Philippe Salembier, Jocelyn Chanussot
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