no code implementations • 2 Oct 2020 • Guillaume Noyel, Jesus Angulo, Dominique Jeulin
The present paper develops a general methodology for the morphological segmentation of hyperspectral images, i. e., with an important number of channels.
no code implementations • 28 Oct 2019 • Guillaume Noyel, Jesus Angulo, Dominique Jeulin, Daniel Balvay, Charles-André Cuenod
A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification and stochastic watershed is explained and tested on several data sets.
no code implementations • 9 Feb 2016 • Guillaume Noyel, Jesus Angulo, Dominique Jeulin
Subsequently, a probability density function (pdf) of contours containing spatial and spectral information is estimated by simulation using a stochastic WS approach driven by the spectral classification.
no code implementations • 2 Feb 2016 • Guillaume Noyel, Jesus Angulo, Dominique Jeulin
Then a finer segmentation is obtained by computing $\eta$-bounded regions and $\mu$-geodesic balls inside the $\lambda$-flat zones.