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

• Type: Journal
• Year: 2010
• Where: IGARSS
• Authors: Silvia Valero, Philippe Salembier, Jocelyn Chanussot
• Keywords: Binary Partition Trees, Formal Methods, Hierarchical Structure Representation, Hyperspectral Data, IGARSS 2010, Remote Sensing