Contrastive Learning Based on Transformer for Hyperspectral Image Classification
Source abstract
Abstract
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 unsupervised 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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote-Sensing Image Classification · Remote Sensing and Land Use · Face and Expression Recognition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
The State Key Laboratory of High-Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, China
Beijing Institute for Advanced Study, National University of Defense Technology, Beijing 100020, China
College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China
Funding
National Key Research and Development Program of China · National Natural Science Foundation of China
References (28)
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