A COMPREHENSIVE SURVEY OF ROBUST IMAGE QUALITY METRICS FOR SATELLITE IMAGERY

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Year : 2026 | Volume : 15 | 01 | Page :
    By

    Pooja Pandya,

  • Dr Bhargav Rajyagor,

  1. Assistant Professor, Faculty of Computer Application, Noble University, Junagadh, Gujarat, India
  2. Principal and Associate Professor, Faculty of Computer Application, Noble University, Junagadh, Gujarat, India

Abstract

Satellite imagery is essential for applications like environmental monitoring, urban development, precision agriculture, defence surveillance, and disaster response. The reliability of these applications is closely tied to the quality of the captured images, which may be compromised by atmospheric effects, sensor imperfections, compression artifacts, and transmission noise. As a result, accurate image quality assessment (IQA) is essential to ensure trustworthy analysis and informed decision-making in satellite-based systems. The distinctive properties of remote sensing data—including high spatial resolution, multispectral and hyperspectral characteristics, and application-specific requirements—introduce additional complexity to conventional IQA methods. This paper delivers a detailed review and analytical evaluation of IQA techniques tailored for satellite and remote sensing imagery. Its primary aim is to systematically classify, compare, and assess existing quality assessment methods, highlighting their advantages, limitations, 2 and practical relevance. The reviewed approaches are grouped into three main categories: full-reference (FR), reduced-reference (RR), and no-reference (NR) metrics. Within these groups, the study examines traditional statistical measures such as mean squared error (MSE) and peak signal-to-noise ratio (PSNR), structural similarity-based metrics, information- theoretic models, perceptual quality indicators, and recent deep learning-based approaches. The paper also addresses task-oriented evaluation frameworks and emerging methods designed for multispectral, hyperspectral, and super-resolution satellite imagery. A comparative analysis is performed based on criteria such as robustness to various distortions, computational efficiency, scalability, and suitability for operational satellite systems. The findings reveal that although classical FR metrics are computationally simple and widely used, they often show limited correlation with perceptual and application-driven quality in remote sensing contexts. Structural and perceptual metrics improve correlation but may not fully capture spectral complexity. Deep learning-based NR methods demonstrate strong adaptability and prediction accuracy in challenging distortion conditions; however, they demand substantial annotated data and computational resources. Task-driven evaluation strategies appear promising in aligning image quality assessment with mission-specific performance objectives. In conclusion, no single IQA method can comprehensively address all the requirements of satellite imagery analysis. Future research should emphasize the development of adaptive, intelligent, and data-driven frameworks capable of handling multimodal satellite data and real-time operational constraints. The integration of domain expertise with advanced learning techniques, along with the creation of standardized benchmark datasets, will be crucial for advancing robust and practical satellite image quality assessment solutions

Keywords: Satellite imagery, Image Quality Assessment (IQA), Remote sensing, Robust metrics, Full-reference metrics, No-reference metrics, Deep learning, Multispectral imaging, Hyperspectral imaging.

How to cite this article:
Pooja Pandya, Dr Bhargav Rajyagor. A COMPREHENSIVE SURVEY OF ROBUST IMAGE QUALITY METRICS FOR SATELLITE IMAGERY. Research & Reviews : Journal of Space Science & Technology. 2026; 15(01):-.
How to cite this URL:
Pooja Pandya, Dr Bhargav Rajyagor. A COMPREHENSIVE SURVEY OF ROBUST IMAGE QUALITY METRICS FOR SATELLITE IMAGERY. Research & Reviews : Journal of Space Science & Technology. 2026; 15(01):-. Available from: https://journals.stmjournals.com/rrjosst/article=2026/view=240903


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Ahead of Print Subscription Review Article
Volume 15
01
Received 19/03/2026
Accepted 23/04/2026
Published 24/04/2026
Publication Time 36 Days
4

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