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.

本文提出了一种结合对比度学习和自编码器的无监督特征学习方法，称为 prototypical contrastive learning，用于高光谱分类，该方法通过两个自编码器网络和对比学习网络的优化来提高表征能力，取得了比其他对比方法和某些监督方法更好的性能表现，并在保持性能的同时提高了特征提取速度并减少了计算资源需求，为无监督对比学习的研究提供了方便。

基于自编码器和原型对比学习的高光谱分类的无监督特征学习

Unsupervised Feature Learning by Autoencoder and Prototypical  Contrastive Learning for Hyperspectral Classification

In this paper, we tackle the question of discovering an effective set of
spatial filters to solve hyperspectral classification problems. Instead of
fixing a priori the filters and their parameters using expert knowledge, we let
the model find them within random draws in the (possibly infinite) space of
possible filters. We define an active set feature learner that includes in the
model only features that improve the classifier. To this end, we consider a
fast and linear classifier, multiclass logistic classification, and show that
with a good representation (the filters discovered), such a simple classifier
can reach at least state of the art performances. We apply the proposed active
set learner in four hyperspectral image classification problems, including
agricultural and urban classification at different resolutions, as well as
multimodal data. We also propose a hierarchical setting, which allows to
generate more complex banks of features that can better describe the
nonlinearities present in the data.

本文针对通过空间滤波器解决高光谱分类问题，提出了一种使用数据驱动方法找到滤波器的有效集合的方法，并在四个高光谱图像分类问题上实现了良好的分类性能。