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Generative adversarial networks (GANs) have shown striking performances in computer vision applications to augment virtual training samples (VTS). However, the VTS generating by GANs in the context of hyperspectral image classification suffer from structural inconsistency due to the insufficient number of training samples in order to learn high-order features from the discriminator. This work addresses the scarcity of training samples by designing a GAN, in which the performance of discriminator is improved to produce more structurally coherent VTS. In the proposed method, by splitting the discriminator into two parts, GAN undertakes two tasks: the main task is to learn to distinguish between real and fake samples, and the auxiliary task is to learn to distinguish structurally corrupted and real samples. With this setup, GAN will produce real-like VTS with a higher variation than conventional GAN. Furthermore, in order to reduce the computational cost, subspace-based dimension reduction was performed to obtain the dominant features around the training samples to generate meaningful patterns from the original ones to be used in the learning phase. Based on the experimental results on real, and well-known hyperspectral benchmark images, the proposed method improves the performance compared with GANs-related, and conventional data augmentation strategies1. |
