Abstract
In this paper, we propose a novel dictionary learning method for hyperspectral image classification. The proposed method, linear regression Fisher discrimination dictionary learning (LRFDDL), obtains a more discriminative dictionary and a classifier by incorporating linear regression term and the Fisher discrimination into the objective function during training. The linear regression term makes predicted and actual labels as close as possible; while the Fisher discrimination is imposed on the sparse codes so that they have small with-class scatters but large between-class scatters. Experiments show that LRFDDL significantly improves the performances of hyperspectral image classification.
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Chen, L., Yang, M., Deng, C., Yin, H. (2014). Linear Regression Fisher Discrimination Dictionary Learning for Hyperspectral Image Classification. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_31
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DOI: https://doi.org/10.1007/978-3-319-10840-7_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10839-1
Online ISBN: 978-3-319-10840-7
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