  Hyperspectral imaging is a rich source of data, allowing for multitude of
effective applications. However, such imaging remains challenging because of
large data dimension and, typically, small pool of available training examples.
While deep learning approaches have been shown to be successful in providing
effective classification solutions, especially for high dimensional problems,
unfortunately they work best with a lot of labelled examples available. To
alleviate the second requirement for a particular dataset the transfer learning
approach can be used: first the network is pre-trained on some dataset with
large amount of training labels available, then the actual dataset is used to
fine-tune the network. This strategy is not straightforward to apply with
hyperspectral images, as it is often the case that only one particular image of
some type or characteristic is available. In this paper, we propose and
investigate a simple and effective strategy of transfer learning that uses
unsupervised pre-training step without label information. This approach can be
applied to many of the hyperspectral classification problems. Performed
experiments show that it is very effective at improving the classification
accuracy without being restricted to a particular image type or neural network
architecture. The experiments were carried out on several deep neural network
architectures and various sizes of labeled training sets. The greatest
improvement in overall accuracy on the Indian Pines and Pavia University
datasets is over 21 and 13 percentage points, respectively. An additional
advantage of the proposed approach is the unsupervised nature of the
pre-training step, which can be done immediately after image acquisition,
without the need of the potentially costly expert's time.
