Unsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification
This is an incremental improvement for hyperspectral classification researchers, reducing computing resource requirements and enabling broader experimentation with unsupervised contrastive learning.
The paper tackles unsupervised feature learning for hyperspectral classification by combining autoencoder and prototypical contrastive learning, resulting in a method that surpasses other comparison methods, including some supervised ones, while maintaining fast feature extraction speed.
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.