Image Quality Assessment Methods for Multispectral Pan-Sharpening Images: A Comprehensive Review

Abstract

Multispectral pan-sharpening aims to fuse high-resolution panchromatic and low-resolution multispectral imagery. However, this process introduces spatial artifacts and spectral distortions. Assessing the quality of fused images remains a fundamental challenge due to the absence of full-resolution ground-truth data. This paper provides a comprehensive review of Image Quality Assessment (IQA) frameworks tailored for pan-sharpened imagery. After overviewing major fusion approaches, including Component Substitution (CS), Multi-Resolution Analysis (MRA), Variational Optimization (VO), and Deep Learning (DL), the review analyzes the evaluation techniques used to benchmark them. It then systematically examines the evolution of evaluation protocols, from classical reference-based metrics relying on Wald’s protocol to full-resolution consistency models and recent no-reference (NR) algorithms. The analysis highlights critical methodological bottlenecks within the field, including unrealistic scale-invariance assumptions in consistency-based metrics, dependence on arbitrary parameters, and severe cross-sensor overfitting in deep learning approaches. Furthermore, the review addresses the mismatch between mathematical fidelity, human visual perception, and practical applicability. Finally, it outlines future research directions, focusing on spatial quality mapping and task-driven assessment protocols that validate fusion efficacy based on its impact on automated remote sensing applications.