  Employing deep neural networks for Hyperspectral remote sensing (HSRS) image
classification is a challenging task. HSRS images have high dimensionality and
a large number of channels with substantial redundancy between channels. In
addition, the training data for classifying HSRS images is limited and the
amount of available training data is much smaller compared to other
classification tasks. These factors complicate the training process of deep
neural networks with many parameters and cause them to not perform well even
compared to conventional models. Moreover, convolutional neural networks
produce over-confident predictions, which is highly undesirable considering the
aforementioned problem.
  In this work, we use for HSRS image classification a special class of deep
neural networks, namely a Bayesian neural network (BNN). To the extent of our
knowledge, this is the first time that BNNs are used in HSRS image
classification. BNNs inherently provide a measure for uncertainty. We perform
extensive experiments on the Pavia Centre, Salinas, and Botswana datasets. We
show that a BNN outperforms a standard convolutional neural network (CNN) and
an off-the-shelf Random Forest (RF). Further experiments underline that the BNN
is more stable and robust to model pruning, and that the uncertainty is higher
for samples with higher expected prediction error.
