HyperBench: Standardizing and Scaling Synthetic Evaluation for Hyperspectral Super-Resolution

TL;DR

HyperBench is a comprehensive framework that standardizes and scales synthetic evaluation for hyperspectral super-resolution, enabling fairer, more reproducible, and extensive comparisons of HSR methods under diverse realistic conditions.

Contribution

It introduces HyperBench, a flexible, extensible platform for standardized synthetic experiments in HSR, facilitating reproducible and large-scale method evaluation.

Findings

Evaluation reveals significant performance variability across different PSFs.

Current single-configuration assessments underestimate method fragility.

HyperBench enables detailed analysis of method robustness under diverse conditions.

Abstract

Hyperspectral super-resolution (HSR) reconstructs a high-spatial-resolution hyperspectral image by fusing a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI). In the absence of real-world paired data, HSR methods are evaluated almost exclusively on synthetic experiments derived from hyperspectral datasets through Wald's protocol. Despite the protocol's widespread adoption, its practical implementation varies markedly across research works, typically relying on a single (usually Gaussian) or very few point spread functions (PSFs), one or two spectral response functions (SRFs), and a couple of spatial downsampling factors. As a result, reported performance figures are difficult to compare across the literature, in addition to being often difficult to reproduce; furthermore, they may not generalize across realistic sensing conditions. We introduce HyperBench, a unified and extensible framework that standardizes synthetic experimentation for HSR. HyperBench supports diverse degradation configurations spanning ten PSFs, four SRFs derived from operational multispectral sensors, configurable spatial downsampling factors, and matched additive white Gaussian noise; its goal is to automate large-scale evaluation and structured logging. By decoupling model development from experimental design, the framework enables reproducible, apples-to-apples cross-method comparison with minimal friction. We use HyperBench to evaluate six recently proposed HSR methods across a 70-configuration sweep on four widely used hyperspectral scenes and observe that the inter-method PSNR spread widens from approximately 5 dB on the easiest PSF to over 13 dB on the hardest - a fragility that is structurally invisible to the prevailing single-configuration evaluation protocol. HyperBench code is available at https://github.com/ritikgshah/HyperBench .