Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification
This addresses data imbalance in hyperspectral image classification, which is a domain-specific problem, but the approach appears incremental as it builds on existing GAN frameworks.
The paper tackles imbalance hyperspectral image classification by proposing a multi-fake evolutionary generative adversarial network (MFEGAN), which uses different generative objective losses to improve discriminator performance, and reports that it outperforms state-of-the-art methods on two hyperspectral datasets.
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.