Comparison of merging orders and pruning strategies for binary partition tree in hyperspectral data

Abstract : Hyperspectral imaging segmentation has been an active research area over the past few years. Despite the growing interest, some factors such as high spectrum variability are still significant issues. In this work, we propose to deal with segmentation through the use of Binary Partition Trees (BPTs). BPTs are suggested as a new representation of hyperspectral data representation generated by a merging process. Different hyperspectral region models and similarity metrics defining the merging orders are presented and analyzed. The resulting merging sequence is stored in a BPT structure which enables image regions to be represented at different resolution levels. The segmentation is performed through an intelligent pruning of the BPT, that selects regions to form the final partition. Experimental results on two hyperspectral data sets have allowed us to compare different merging orders and pruning strategies demonstrating the encouraging performances of BPT-based representation.
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Silvia Valero, Philippe Salembier, Jocelyn Chanussot. Comparison of merging orders and pruning strategies for binary partition tree in hyperspectral data. 17th IEEE International Conference on Image Processing (ICIP 2010), Sep 2010, Hong Kong, Hong Kong SAR China. conference proceedings. ⟨hal-00578963⟩

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