  The amount of training data that is required to train a classifier scales
with the dimensionality of the feature data. In hyperspectral remote sensing,
feature data can potentially become very high dimensional. However, the amount
of training data is oftentimes limited. Thus, one of the core challenges in
hyperspectral remote sensing is how to perform multi-class classification using
only relatively few training data points.
  In this work, we address this issue by enriching the feature matrix with
synthetically generated sample points. This synthetic data is sampled from a
GMM fitted to each class of the limited training data. Although, the true
distribution of features may not be perfectly modeled by the fitted GMM, we
demonstrate that a moderate augmentation by these synthetic samples can
effectively replace a part of the missing training samples. We show the
efficacy of the proposed approach on two hyperspectral datasets. The median
gain in classification performance is $5\%$. It is also encouraging that this
performance gain is remarkably stable for large variations in the number of
added samples, which makes it much easier to apply this method to real-world
applications.
