# Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification Source: [Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:15:34.723Z Authors: Tanmoy Dam, Nidhi Swami, Sreenatha G. Anavatti, Hussein A. Abbass Published: 2021-11-07T07:29:24.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()