Oriented Triplet Markov Field for Hyperspectral Image Segmentation

Jean-Baptiste Courbot (1, 2) , Emmanuel Monfrini (3, 4) , Vincent Mazet (1) , Christophe Collet (1)

1 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
2 CRAL - Centre de Recherche Astrophysique de Lyon
3 TIPIC-SAMOVAR - Traitement de l'Information Pour Images et Communications
4 CNRS - Centre National de la Recherche Scientifique

Hyperspectral image processing benefits greatly from using spatial information. Markov field modeling is a well-known statistical model class for considering spatial relationships between sites of an image. Often, the model restricts to Hidden Markov Field, therefore cannot handle non-stationarities in the images. This paper presents a Triplet Markov Field model for hyperspectral image segmentation, allowing the joint retrieving of image classes and local orientations. Segmentation results on synthetic data validate the methods, and results on real astronomical data are presented.

Mots clés: Triplet Markov Field; Bayesian Segmentation; Orientation Retrieving

Domaines: Applications [stat.AP]; Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]

Jean-Baptiste Courbot, Emmanuel Monfrini, Vincent Mazet, Christophe Collet. Oriented Triplet Markov Field for Hyperspectral Image Segmentation. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing, Aug 2016, Los Angeles, United States. ⟨hal-01421883⟩