  We address two questions for training a convolutional neural network (CNN)
for hyperspectral image classification: i) is it possible to build a
pre-trained network? and ii) is the pre-training effective in furthering the
performance? To answer the first question, we have devised an approach that
pre-trains a network on multiple source datasets that differ in their
hyperspectral characteristics and fine-tunes on a target dataset. This approach
effectively resolves the architectural issue that arises when transferring
meaningful information between the source and the target networks. To answer
the second question, we carried out several ablation experiments. Based on the
experimental results, a network trained from scratch performs as good as a
network fine-tuned from a pre-trained network. However, we observed that
pre-training the network has its own advantage in achieving better performances
when deeper networks are required.
