Details

Autor(en) / Beteiligte

• Jun Li
• Bioucas-Dias, J.M.
• Plaza, A.

Titel

Semi-supervised hyperspectral image segmentation

Ist Teil von

• 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009, p.1-4

Ort / Verlag

IEEE

Erscheinungsjahr

2009

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• This paper presents a new semi-supervised segmentation algorithm, suited to high dimensional data, of which hyperspectral images are an example. The algorithm implements two main steps: (a) semi-supervised learning, used 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. The class distributions are modeled with a multinomial logistic regression, where the regressors are learned using both labeled and, through a graph-based technique, unlabeled samples. The prior on the labels is a multi-level logistic model. The maximum a posterior segmentation is computed by the alpha-Expansion min-cut based integer optimization algorithm. We give experimental evidence that the spatial prior greatly improves the segmentation performance, with respect to that of a semi-supervised classifier. The effectiveness of the proposed method is demonstrated with simulated and real data.