Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance
Hyperspectral Image Classification
- URL: http://arxiv.org/abs/2111.04019v2
- Date: Mon, 20 Mar 2023 06:26:10 GMT
- Title: Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance
Hyperspectral Image Classification
- Authors: Tanmoy Dam, Nidhi Swami, Sreenatha G. Anavatti, Hussein A. Abbass
- Abstract summary: This paper presents a novel multi-fake evolutionary generative adversarial network for handling imbalance hyperspectral image classification.
Different generative objective losses are considered in the generator network to improve the classification performance of the discriminator network.
The effectiveness of the proposed method has been validated through two hyperspectral spatial-spectral data sets.
- Score: 7.9067022260826265
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents a novel multi-fake evolutionary generative adversarial
network(MFEGAN) for handling imbalance hyperspectral image classification. It
is an end-to-end approach in which different generative objective losses are
considered in the generator network to improve the classification performance
of the discriminator network. Thus, the same discriminator network has been
used as a standard classifier by embedding the classifier network on top of the
discriminating function. The effectiveness of the proposed method has been
validated through two hyperspectral spatial-spectral data sets. The same
generative and discriminator architectures have been utilized with two
different GAN objectives for a fair performance comparison with the proposed
method. It is observed from the experimental validations that the proposed
method outperforms the state-of-the-art methods with better classification
performance.
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