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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.