Hyperspectral image representation through alpha-trees

François Merciol (1) , Laëtitia Chapel (1) , Sébastien Lefèvre (1)

1 OBELIX - Environment observation with complex imagery

α-trees provide a hierarchical representation of an image into partitions of regions with increasing heterogeneity. This model, inspired from the single-linkage paradigm, has recently been revisited for grayscale images and has been successfully used in the field of remote sensing. This article shows how this representation can be adapted to more complex data here hyperspectral images, according to different strategies. We know that the measure of distance between two neighbouring pixels is a key element for the quality of the underlying tree, but usual metrics are not satisfying. We show here that a relevant solution to understand hyperspectral data relies on the prior learning of the metric to be used and the exploitation of domain knowledge.

Mots clés: Index Terms-Hyperspectral Images; Metric Learning; Hierarchical Representations; α-Trees

Domaines: Traitement des images [eess.IV]

François Merciol, Laëtitia Chapel, Sébastien Lefèvre. Hyperspectral image representation through alpha-trees. ESA-EUSC-JRC 9th Conference on Image Information Mining, 2014, Bucarest, Romania. pp.37-40, ⟨10.2788/25852⟩. ⟨hal-00998255⟩