TY - GEN
T1 - Oriented Triplet Markov fields for hyperspectral image segmentation
AU - Courbot, Jean Baptiste
AU - Monfrini, Emmanuel
AU - Mazet, Vincent
AU - Collet, Christophe
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/28
Y1 - 2016/6/28
N2 - 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.
AB - 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.
KW - Bayesian Segmentation
KW - Orientation Retrieving
KW - Triplet Markov Field
UR - https://www.scopus.com/pages/publications/85037530641
U2 - 10.1109/WHISPERS.2016.8071755
DO - 10.1109/WHISPERS.2016.8071755
M3 - Conference contribution
AN - SCOPUS:85037530641
T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
BT - 2016 8th Workshop on Hyperspectral Image and Signal Processing
PB - IEEE Computer Society
T2 - 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016
Y2 - 21 August 2016 through 24 August 2016
ER -