Semi-Supervised Classification of Hyperspectral Images based on Contrastive Learning Constraint

Junyuan Ding, Yue Wen, Weixin Ren, Lei Zhang, Wei Wei

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Despite significant advancements in deep learning-based algorithms for classifying hyperspectral image (HSI), this task remains challenging when only few labeled training examples are available. In this paper, we introduce a contrastive learning constraint and propose a semi-supervised HSI classification approach. We first build a multi-scale feature extraction module, which extracts fine-grained features from a small number of labeled samples together with a huge amount of unlabeled samples. Then, by modeling contrastive constraints on the unlabeled data, we construct a contrastive sub-network module, which can efficiently support the supervised HSI classification sub-network trained on the labeled dataset and hence enhance the generalization ability. Experimental results on two datasets demonstrate the effectiveness of the proposed semi-supervised HSI classification methods.

源语言英语
主期刊名IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
7273-7276
页数4
ISBN(电子版)9798350320107
DOI
出版状态已出版 - 2023
活动2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, 美国
期限: 16 7月 202321 7月 2023

出版系列

姓名International Geoscience and Remote Sensing Symposium (IGARSS)
2023-July

会议

会议2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
国家/地区美国
Pasadena
时期16/07/2321/07/23

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