Generating images for Supervised Hyperspectral Image Classification with Generative Adversarial Nets

Abstract

With the advancement of remote sensing technologies, hyperspectral imagery has garnered significant interest in the remote sensing community. These developments have inspired improvement in various hyperspectral images (HSI) classification applications, such as land cover mapping, amongst other earth observation applications. Deep Neural Networks have revolutionized image classification tasks in areas of computer vision. However, in the domain of hyperspectral images, insufficient training samples have been earmarked as a significant bottleneck for supervised HSI classification. Moreover, acquiring HSI from satellites and other remote sensors is expensive. Thus, researchers have turned to generative models to leverage the existing data to increase training samples, such as particularly generative adversarial networks (GAN). This paper explores the use of a vanilla GAN to generate synthetic data. The network employed in this paper was built using a deep learning python package, PyTorch and tested on a popular HSI dataset called Indian Pines dataset. The network achieved an overall accuracy of 64%. While promising, there is still room for improvement.