Class-Specific Autoaugment Architecture Based on Schmidt Mathematical Theory for Imbalanced Hyperspectral Classification

Hyperspectral image classification (HSIC) often suffers from severe imbalanced category distribution in real applications, which causes bias toward the dominated categories. As an effective method, the deep generative model (DGM) can be used to augment the features of imbalanced data through a learn...

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Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 61; s. 1 - 15
Hlavní autoři:	Li, Jiaojiao, Diao, Yan, Song, Rui, Xi, Bobo, Li, Yunsong, Du, Qian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Categories Classification Classifiers Data mining Data models Feature extraction Gaussian distribution Hyperspectral imaging Image classification Image reconstruction Imaging Imbalanced data interclass difference Mathematical analysis Normal distribution Prototypes Schmidt mathematical theory Theories Training data weight balance
ISSN:	0196-2892, 1558-0644
On-line přístup:	Získat plný text
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