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Class-Wise Distribution Adaptation for Unsupervised Classification of Hyperspectral Remote Sensing Images
IEEE Transactions on Geoscience and Remote Sensing ( IF 8.2 ) Pub Date : 2020-06-09 , DOI: 10.1109/tgrs.2020.2997863 Zixu Liu , Li Ma , Qian Du
IEEE Transactions on Geoscience and Remote Sensing ( IF 8.2 ) Pub Date : 2020-06-09 , DOI: 10.1109/tgrs.2020.2997863 Zixu Liu , Li Ma , Qian Du
Class-wise adversarial adaptation networks are investigated for the classification of hyperspectral remote sensing images in this article. By adversarial learning between the feature extractor and the multiple domain discriminators, domain-invariant features are generated. Moreover, a probability-prediction-based maximum mean discrepancy (MMD) method is introduced to the adversarial adaptation network to achieve a superior feature-alignment performance. The class-wise adversarial adaptation in conjunction with the class-wise probability MMD is denoted as the class-wise distribution adaptation (CDA) network. The proposed CDA does not require labeled information in the target domain and can achieve an unsupervised classification of the target image. The experimental results using the Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data demonstrated its efficiency.
中文翻译:
高光谱遥感影像无监督分类的分类明智分布
本文针对高光谱遥感影像的分类研究了类对抗对抗网络。通过特征提取器与多个域区分符之间的对抗学习,可以生成域不变特征。此外,将基于概率预测的最大均值差异(MMD)方法引入对抗适应网络,以实现出色的特征对齐性能。与分类概率MMD结合的分类对抗适应性表示为分类分布适应(CDA)网络。提议的CDA不需要目标域中的标记信息,并且可以实现目标图像的无监督分类。
更新日期:2020-06-09
中文翻译:
高光谱遥感影像无监督分类的分类明智分布
本文针对高光谱遥感影像的分类研究了类对抗对抗网络。通过特征提取器与多个域区分符之间的对抗学习,可以生成域不变特征。此外,将基于概率预测的最大均值差异(MMD)方法引入对抗适应网络,以实现出色的特征对齐性能。与分类概率MMD结合的分类对抗适应性表示为分类分布适应(CDA)网络。提议的CDA不需要目标域中的标记信息,并且可以实现目标图像的无监督分类。