A Comparison of Hyperspectral Super-Resolution Techniques for Boreal Forest Imagery

Abstract

Despite the widespread use of deep learning models for super-resolution image enhancement, their use for hyper-spectral imagery has not yet been researched thoroughly. This study reviews a number of recent hyperspectral image super-resolution techniques and explores also other single-image super-resolution methods. Our work targets to forestry images, highlighting the main methodologies, contributions, advantages, and limitations of the studied methods. The state-of-the-art methods are categorized into three distinct groups, those based on the Convolutional Neural Network (CNN), the Transformer, and the Generative Adversarial Network (GAN). Subsequently, the selected methods are compared in terms of six different performance measures on an airborne hyperspectral image dataset of a boreal forest. Our findings conclude that Transformer-based methods consistently outperform other current hyperspectral super-resolution techniques, while the GAN approach is the most promising one among the studied non-hyperspectral models.