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Contrastive Learning Based on Transformer for Hyperspectral Image Classification
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
doi:10.3390/app11188670
fatcat:vq4s7lw6hbgffjipaw2cwypkmq