Hyperspectral Image Super-Resolution: Task-Based Evaluation

Abstract

The need for enhancing image spatial resolution has motivated the researchers to propose numerous super-resolution (SR) techniques, including those developed specifically for hyperspectral data. Despite significant advancements in this field attributed to deep learning, little attention has been given to evaluating the practical value of super-resolved images in specific applications. Most methods are validated in application-independent scenarios, often using simulated low-resolution images, resulting in overly optimistic conclusions. In this article, we propose task-based evaluation strategies for hyperspectral image SR and we present results obtained with various approaches that include pansharpening, multispectral–hyperspectral data fusion, and single-image SR. We demonstrate that the proposed framework allows us to highlight both benefits and limitations of each method and can, therefore, guide the development of SR techniques suitable for real-world applications.