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Proceedings Paper

Semi-supervised hyperspectral classification using active label selection
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Paper 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.

Paper Details

Date Published: 28 September 2009
PDF: 8 pages
Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74770F (28 September 2009); doi: 10.1117/12.830509
Show Author Affiliations
Jun Li, Instituto de Telecomunicações, Instiuto Superior Técnico, Univ. Técnica de Lisboa (Portugal)
José Bioucas-Dias, Instituto de Telecomunicações, Instiuto Superior Técnico, Univ. Técnica de Lisboa (Portugal)
Antonio Plaza, Univ. de Extremadura (Spain)


Published in SPIE Proceedings Vol. 7477:
Image and Signal Processing for Remote Sensing XV
Lorenzo Bruzzone; Claudia Notarnicola; Francesco Posa, Editor(s)

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