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A No-Reference Multivariate Gaussian-Based Spectral Distortion Index for Pansharpened Images
Bishr Omer Abdelrahman Adam1, Xu Li1, Jingying Wu1
1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China.
Sensors (Basel, Switzerland)
|February 13, 2026
Summary
This study introduces a new method, the Multivariate Gaussian-based Spectral Distortion Index (MVG-SDI), to accurately assess spectral distortions in pansharpened remote sensing images without needing a reference image.
Area of Science:
- Remote Sensing
- Image Processing
- Geospatial Analysis
Background:
- Pansharpening enhances remote sensing image spatial resolution but introduces spectral distortions.
- Existing no-reference quality assessment methods struggle to isolate spectral errors from spatial artifacts.
Purpose of the Study:
- To propose a novel No-Reference Multivariate Gaussian-based Spectral Distortion Index (MVG-SDI) for pansharpened images.
- To accurately quantify spectral distortions without reference data.
Main Methods:
- Extracting hybrid features: First Digit Distribution (FDD) from Benford's Law and Color Moments (CM).
- Fitting Multivariate Gaussian (MVG) models to original multispectral and fused images.
- Quantifying spectral distortion using Mahalanobis distance between MVG model parameters.
Main Results:
- MVG-SDI shows stronger correlation with full-reference benchmarks (SAM, CC) than existing NR methods (e.g., QNR).
- The index demonstrates stability and accuracy in detecting specific spectral degradations like hue shifts and saturation changes.
Conclusions:
- The proposed MVG-SDI effectively assesses spectral distortion in pansharpened images.
- This method offers a reliable tool for evaluating remote sensing data quality.
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