Source-Free Domain Adaptation for Cross-Scene Hyperspectral Image Classification

Zun Xu, Wei Wei, Lei Zhang, Jiangtao Nie

科研成果: 书/报告/会议事项章节会议稿件同行评审

9 引用 (Scopus)

摘要

Deep learning based cross-domain hyperspectral image (HSI) classification methods were proposed to train a classifier adapted to unlabeled target domain with the help of abundant labeled data in source domain. Although the existing methods show their potential for cross-domain HSI classification, the data in source domain may not be provided due to the data privacy, which limits the availability of these methods. In this case, how to utilize the model or knowledge trained from source domain becomes a more challenging problem. In this study, we emphasize on this problem, and propose source-free unsupervised domain adaptation method for HSI classification. Specifically, we firstly design a source domain HSI spectral feature generator, and then realize the class-wised alignment between the generated source domain HSI spectral features and the target domain features of HSI through contrastive learning. To solve the dilemma of without labels in the target domain, we also utilize a logits-weighted prototype classifier to iteratively obtain the data label of the target domain. Experiments on two cross-scene HSI datasets demonstrate the effectiveness of the proposed method when only providing the model trained from the source domain.

源语言英语
主期刊名IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
出版商Institute of Electrical and Electronics Engineers Inc.
3576-3579
页数4
ISBN(电子版)9781665427920
DOI
出版状态已出版 - 2022
活动2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, 马来西亚
期限: 17 7月 202222 7月 2022

出版系列

姓名International Geoscience and Remote Sensing Symposium (IGARSS)
2022-July

会议

会议2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
国家/地区马来西亚
Kuala Lumpur
时期17/07/2222/07/22

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