Unsupervised Adversarial Domain Adaptation with Error-Correcting Boundaries and Feature Adaption Metric for Remote-Sensing Scene Classification

Unsupervised domain adaptation (UDA) based on adversarial learning for remote-sensing scene classification has become a research hotspot because of the need to alleviating the lack of annotated training data. Existing methods train classifiers according to their ability to distinguish features from...

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Автори:	Chenhui Ma, Dexuan Sha, Xiaodong Mu
Формат:	Artigo
Мова:	Inglês
Опубліковано:	MDPI AG 2021-03-01
Серія:	Remote Sensing
Предмети:	remote-sensing scene classification adversarial learning unsupervised domain adaptation target-domain-specific classifier boundaries domain invariant features
Онлайн доступ:	https://www.mdpi.com/2072-4292/13/7/1270
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