  This paper presents a self-supervised feature learning method for
hyperspectral image classification. Our method tries to construct two different
views of the raw hyperspectral image through a cross-representation learning
method. And then to learn semantically consistent representation over the
created views by contrastive learning method. Specifically, four
cross-channel-prediction based augmentation methods are naturally designed to
utilize the high dimension characteristic of hyperspectral data for the view
construction. And the better representative features are learned by maximizing
mutual information and minimizing conditional entropy across different views
from our contrastive network. This 'Cross-View-Predicton' style is
straightforward and gets the state-of-the-art performance of unsupervised
classification with a simple SVM classifier.
