论文标题
自动编码器的无监督功能学习和高光谱分类的原型对比度学习
Unsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification
论文作者
论文摘要
无监督的特征提取学习方法变得越来越流行。我们结合了流行的对比度学习方法（原型对比度学习）和经典表示学习方法（自动编码器），以设计用于高光谱分类的无监督功能学习网络。实验证明，我们提出的两个自动编码器网络本身具有良好的功能学习能力，我们设计的对比度学习网络可以更好地结合两者的功能，以学习更多代表性的功能。结果，我们的方法超过了高光谱分类实验中的其他比较方法，包括一些监督方法。此外，我们的方法比基线方法保持快速的特征提取速度。此外，我们的方法减少了对庞大计算资源的要求，将特征提取和对比度学习分开，并允许更多的研究人员对无监督的对比学习进行研究和实验。
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.
