New hyperspectral data representation using binary partition tree

Silvia Valero (1, 2) , Philippe Salembier (2) , Jocelyn Chanussot (1)

1 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
2 UPC - Universitat Politècnica de Catalunya = Université polytechnique de Catalogne [Barcelona]

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.

Mots clés: Hyperspectral imaging; Binary Partition Tree; segmentation; classification

Domaines: Traitement des images [eess.IV]

Silvia Valero, Philippe Salembier, Jocelyn Chanussot. New hyperspectral data representation using binary partition tree. IGARSS 2010 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2010, Honolulu, Hawaii, United States. conference proceedings. ⟨hal-00578960⟩