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

NSIQ: A Physically Grounded Dataset for Image Quality Assessment in Near-Space Hyperspectral Interferometric Imaging | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article NSIQ: A Physically Grounded Dataset for Image Quality Assessment in Near-Space Hyperspectral Interferometric Imaging Cheng Jiang, Chiming Tong, Zhongqi Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7868618/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract 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. Physical sciences/Engineering Physical sciences/Optics and photonics Image quality assessment Near-space hyperspectral interferometric imaging Benchmark dataset Remote sensing image Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Dec, 2025 Reviews received at journal 19 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers invited by journal 29 Oct, 2025 Editor invited by journal 29 Oct, 2025 Editor assigned by journal 23 Oct, 2025 Submission checks completed at journal 23 Oct, 2025 First submitted to journal 15 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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