Abstract
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 \(4.6\%\).
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Davari, A., Christlein, V., Vesal, S., Maier, A., Riess, C. (2017). GMM Supervectors for Limited Training Data in Hyperspectral Remote Sensing Image Classification. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_25
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DOI: https://doi.org/10.1007/978-3-319-64698-5_25
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