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28 September 2009 Semi-supervised hyperspectral classification using active label selection
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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.
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Jun Li, José Bioucas-Dias, and Antonio Plaza "Semi-supervised hyperspectral classification using active label selection", Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74770F (28 September 2009); https://doi.org/10.1117/12.830509
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