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

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]

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.

Mots clés: Binary Partition Tree; Hyperspectral data Segmentation; Merging orders; Pruning strategies

Domaines: Traitement des images [eess.IV]

Silvia Valero, Philippe Salembier, Jocelyn Chanussot. Comparison of merging orders and pruning strategies for binary partition tree in hyperspectral data. ICIP 2010 - 17th IEEE International Conference on Image Processing, Sep 2010, Hong Kong, Hong Kong SAR China. conference proceedings. ⟨hal-00578963⟩