  Unsupervised learning methods for feature extraction are becoming more and
more popular. We combine the popular contrastive learning method (prototypical
contrastive learning) and the classic representation learning method
(autoencoder) to design an unsupervised feature learning network for
hyperspectral classification. Experiments have proved that our two proposed
autoencoder networks have good feature learning capabilities by themselves, and
the contrastive learning network we designed can better combine the features of
the two to learn more representative features. As a result, our method
surpasses other comparison methods in the hyperspectral classification
experiments, including some supervised methods. Moreover, our method maintains
a fast feature extraction speed than baseline methods. In addition, our method
reduces the requirements for huge computing resources, separates feature
extraction and contrastive learning, and allows more researchers to conduct
research and experiments on unsupervised contrastive learning.
