A No-Reference Multivariate Gaussian–Based Spectral Distortion Index for Pansharpened Images | Advanced Image Quality Assessment #WorldResearchAwards

Introduction

Pansharpening plays a crucial role in remote sensing by integrating high-spatial-resolution panchromatic data with lower-resolution multispectral imagery to produce visually rich fused products. Despite its widespread use, pansharpening often introduces spectral distortions that can undermine the reliability of quantitative analyses such as classification, change detection, and biophysical parameter retrieval. Traditional quality assessment approaches, especially full-reference metrics, are limited by the availability of ground truth data, while many no-reference (NR) methods struggle to distinguish spectral distortions from spatial artifacts. This challenge motivates the development of robust NR spectral quality indices tailored specifically for pansharpened imagery.

Limitations of Existing No-Reference Quality Metrics

Current NR quality assessment methods, including widely used indices such as QNR, primarily focus on global consistency or mixed spatial–spectral properties. As a result, they often lack sensitivity to subtle yet critical radiometric inconsistencies introduced during the fusion process. Many of these methods are unable to exclusively isolate spectral distortions, particularly when spatial details are well preserved but spectral fidelity is compromised. This limitation reduces their effectiveness for scientific and operational remote sensing applications where accurate spectral information is essential.

Feature Extraction Using Hyperspherical Color Space

To better capture spectral characteristics, the proposed approach leverages a hybrid feature set extracted in the hyperspherical color space (HCS). First Digit Distribution (FDD) features derived from Benford’s Law are employed to model the intrinsic statistical regularities of spectral data, while Color Moment (CM) features capture fundamental radiometric properties such as mean, variance, and skewness. The combination of these complementary descriptors enables a more discriminative and robust representation of spectral behavior in both original multispectral and pansharpened images.

Multivariate Gaussian Modeling and Spectral Distortion Measurement

The extracted hybrid features are modeled using Multivariate Gaussian (MVG) distributions to characterize the statistical structure of spectral information. Separate MVG models are fitted to the original multispectral image and the corresponding fused product. Spectral distortion is then quantified by computing the Mahalanobis distance between the parameter sets of these models. This distance provides a principled statistical measure of deviation, effectively isolating spectral inconsistencies while minimizing interference from spatial artifacts.

Experimental Validation on Benchmark Datasets

Extensive experiments conducted on the NBU dataset demonstrate the effectiveness of the proposed No-Reference Multivariate Gaussian-based Spectral Distortion Index (MVG-SDI). The results show that MVG-SDI exhibits stronger correlations with established full-reference metrics such as Spectral Angle Mapper (SAM) and Correlation Coefficient (CC) compared to existing NR approaches like QNR. These findings confirm that the proposed index more accurately reflects true spectral fidelity in pansharpened images.

Robustness to Simulated Spectral Distortions

Further validation using controlled simulated distortions highlights the robustness of MVG-SDI under challenging conditions. The index remains stable and sensitive when subjected to specific spectral degradations, including hue shifts and saturation changes, which are commonly encountered in practical pansharpening scenarios. This robustness underscores the potential of MVG-SDI as a reliable NR quality assessment tool for spectral integrity evaluation in remote sensing workflows.

Hashtags

#pansharpening #remotesensing #imagefusion #spectraldistortion #qualityassessment #noreferenceiqm #multivariategaussian #mahalanobisdistance #benfordslaw #colormoments #hypersphericalcolorspace #spectralfidelity #satelliteimagery #geospatialanalysis #radiometricquality #imageprocessing #fusionquality #spectralanalysis #researchinnovation #worldresearchawards

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