NSIQ: A Physically Grounded Dataset for Image Quality Assessment in Near-Space Hyperspectral Interferometric Imaging

Near-space hyperspectral interferometric imaging (20–100 km altitude) is essential for atmospheric observation. It enables high-resolution profiling of greenhouse gases and wind fields. However, this modality is highly vulnerable to nonlinear degradations, including Littrow angle deviations, platform vibrations, and sensor non-uniformities. These factors severely hinder accurate image quality assessment (IQA). Existing IQA benchmarks are primarily built on natural images and lack both physical realism and domain-specific distortions. Consequently, models trained on them often fail to address the physics-driven degradations in interferometric systems. To overcome this limitation, we introduce NSIQ, the first IQA benchmark designed for near-space interferometric imaging.NSIQ contains 201 grayscale interferograms generated with a physics-consistent simulation framework and includes six representative degradation types derived from realistic system-level distortions. Each sample is annotated with hybrid quality labels that combine expert perceptual scores with normalized physical parameters, providing a multi-dimensional view of image quality.Benchmarking results reveal that state-of-the-art IQA methods, while effective on natural-image datasets, suffer substantial performance drops on NSIQ. This highlights the urgent need for domain-adaptive and physically grounded IQA models. The release of NSIQ will facilitate research in environmental monitoring, atmospheric modeling, and intelligent remote sensing. It also provides a foundation for long-term observation and a deeper understanding of the Earth system.