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This paper discusses hierarchical region-based representation using Binary Partition Tree in the framework of hyperspectral data. Based on region merging techniques, this region-based representation reduces the number of elementary primitives compared to the pixel based representation and allows a more robust filtering, segmentation, classification or information retrieval. The work presented here proposes a strategy for merging hyperspectral regions using a new association measure depending on canonical correlations relating principal coordinates. To demonstrate an example of BPT usefulness, a pruning strategy aiming at object detection is discussed. Experimental results demonstrate the good performances of BPT.
CitationValero, S. [et al.]. Improved binary partition tree construction for hyperspectral images: application to object detection. A: IEEE International Geoscience and Remote Sensing Symposium. "Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International". Vancouver: IEEE, 2011, p. 2515-2518.
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