Semi-supervised hyperspectral classification using active label selection

Jun Li; José Bioucas-Dias; Antonio Plaza

doi:10.1117/12.830509

28 September 2009 Semi-supervised hyperspectral classification using active label selection

Jun Li, José Bioucas-Dias, Antonio Plaza

Proceedings Volume 7477, Image and Signal Processing for Remote Sensing XV; 74770F (2009) https://doi.org/10.1117/12.830509
Event: SPIE Remote Sensing, 2009, Berlin, Germany

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.

Citation Download Citation

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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