Semi-Supervised Hyperspectral Image Segmentation
Li, J.
;
Bioucas-Dias, J.
; Plaza, A.
Semi-Supervised Hyperspectral Image Segmentation, Proc IEEE GRSS Workshop on Hyperspectral Image and Signal Processing, Grenoble, France, Vol. 1, pp. 1 - 4, August, 2009.
Digital Object Identifier:
Download Full text PDF ( 214 KBs)
Abstract
This paper introduces a new semi-supervised Bayesian approach to hyperspectral image segmentation. The
algorithm mainly consists of two steps: (a) semi-supervised learning, by using the LORSAL algorithm to infer
the class distributions, followed by (b) segmentation, by inferring the labels from a posterior density built on
the learned class distributions and on a Markov random field. Active label selection is performed. Encouraging
results are presented on real AVIRIS Indiana Pines data set. Comparisons with state-of-the-art algorithms are
also included.