Can I read Unsupervised Meta Learning With Multiview Constraints for Hyperspectral Image Small Sample set Classification on EtoBox?

Unsupervised Meta Learning With Multiview Constraints for Hyperspectral Image Small Sample set Classification by Kuiliang Gao; Bing Liu; Xuchu Yu; Anzhu Yu is a Computer Science article available to read on EtoBox.

What is Unsupervised Meta Learning With Multiview Constraints for Hyperspectral Image Small Sample set Classification about?

The difficulties of obtaining sufficient labeled samples have always been one of the factors hindering deep learning models from obtaining high accuracy in hyperspectral image (HSI) classification. To reduce the dependence of deep learning models on training samples, meta learning methods have been introduced, effectively improving the classification accuracy in small sample set scenarios. However, the existing methods based on meta learning still need to construct a labeled source data set with several pre-collected HSIs, and must utilize a large number of labeled samples for meta-training, which is actually time-consuming and labor-intensive. To solve this problem, this paper proposes a novel unsupervised meta learning method with multiview constraints for HSI small sample set classification. Specifically, the proposed method first builds an unlabeled source data set using unlabeled HSIs. Then, multiple spatial-spectral multiview features of each unlabeled sample are generated to construct tasks for unsupervised meta learning. Finally, the designed residual relation network is used for meta-training and small sample set classification based on the voting strategy. Compared with e

Who reads Unsupervised Meta Learning With Multiview Constraints for Hyperspectral Image Small Sample set Classification?

Author

Kuiliang Gao; Bing Liu; Xuchu Yu; Anzhu Yu

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)