CVA2E: A Conditional Variational Autoencoder With an Adversarial Training Process for Hyperspectral Imagery Classification

Deep generative models such as the generative adversarial network (GAN) and the variational autoencoder (VAE) have obtained increasing attention in a wide variety of applications. Nevertheless, the existing methods cannot fully consider the inherent features of the spectral information, which leads...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 58; no. 8; pp. 5676 - 5692
Main Authors: Wang, Xue, Tan, Kun, Du, Qian, Chen, Yu, Du, Peijun
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Classification
Data models
Gallium nitride
Generative adversarial network (GAN)
Generative adversarial networks
Generators
hyperspectral image (HSI) classification
Hyperspectral imaging
Image classification
Imagery
Spectra
Training
variational autoencoder (VAE)
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:Deep generative models such as the generative adversarial network (GAN) and the variational autoencoder (VAE) have obtained increasing attention in a wide variety of applications. Nevertheless, the existing methods cannot fully consider the inherent features of the spectral information, which leads to the applications being of low practical performance. In this article, in order to better handle this problem, a novel generative model named the conditional variational autoencoder with an adversarial training process (CVA 2 E) is proposed for hyperspectral imagery classification by combining variational inference and an adversarial training process in the spectral sample generation. Moreover, two penalty terms are added to promote the diversity and optimize the spectral shape features of the generated samples. The performance on three different real hyperspectral data sets confirms the superiority of the proposed method.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.2968304