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
Valero et al. REF proposed a region-based hierarchical representation for hyperspectral images based on binary partition tree (BPT).
New hyperspectral data representation using binary partition tree
2010 IEEE International Geoscience and Remote Sensing Symposium
2010 IEEE International Geoscience and Remote Sensing Symposium
Computer Science