Domain Adaptors for Hyperspectral Images

We consider the problem of adapting a network trained on three-channel color images to hyperspectral images with a large number of channels. We propose domain adaptors that map the input to be compatible with a network trained on large-scale color image datasets such as ImageNet, to enable learning on small hyperspectral datasets where training a network from scratch may not be effective. We investigate architectures and strategies for training adaptors and evaluate them on a benchmark consisting of multiple datasets. We find that simple schemes such as linear projection or subset selection are often the most effective, but can lead to a loss in performance in some cases. We also propose a novel multi-view adaptor where of the inputs are combined in an intermediate layer of the network in an order-invariant manner that provides further improvements. We present experiments by varying the number of training examples in the benchmark to characterize the accuracy and computational trade-offs offered by these adaptors.