Contrastive Learning Based on Transformer for Hyperspectral Image Classification

Xiang Hu, Teng Li, Tong Zhou, Yu Liu, Yuanxi Peng
2021 Applied Sciences  
Recently, deep learning has achieved breakthroughs in hyperspectral image (HSI) classification. Deep-learning-based classifiers require a large number of labeled samples for training to provide excellent performance. However, the availability of labeled data is limited due to the significant human resources and time costs of labeling hyperspectral data. Unsupervised learning for hyperspectral image classification has thus received increasing attention. In this paper, we propose a novel
more » ... sed framework based on a contrastive learning method and a transformer model for hyperspectral image classification. The experimental results prove that our model can efficiently extract hyperspectral image features in unsupervised situations.
doi:10.3390/app11188670 fatcat:vq4s7lw6hbgffjipaw2cwypkmq