Severely limited training data is one of the major and most common challenges in the field of hyperspectral remote sensing image classification. Supervised learning on limited training data requires either (a) designing a highly capable classifier that can handle such information scarcity, or (b) designing a highly informative and easily separable feature set. In this paper, we adapt GMM supervectors to hyperspectral remote sensing image features. We evaluate the proposed method on two datasets. In our experiments, inclusion of GMM supervectors leads to a mean classification improvement of about 4.6%.
CITATION STYLE
Davari, A. A., Christlein, V., Vesal, S., Maier, A., & Riess, C. (2017). GMM supervectors for limited training data in hyperspectral remote sensing image classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10425 LNCS, pp. 296–306). Springer Verlag. https://doi.org/10.1007/978-3-319-64698-5_25
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