A Blind Full-Resolution Quality Evaluation Method for Pansharpening

Abstract

Pansharpening methods have been developed for nearly 40 years; however, how to quantitatively evaluate the quality of pansharpened images at full resolution (FR) is probably the most debated topic in this field due to the inherent unavailable of the real HR MS reference image. In this article, a novel blind FR quality evaluation method for pansharpening is proposed. In the proposed method, spatial and spectral features that are sensitive to spatial and spectral distortions of fused images are comprehensively considered and jointly learned based on online multivariate Gaussian (MVG) to construct the evaluation model. It directly outputs the quality of fused images, rather than the stepwise evaluation of spectral score, spatial score, and final overall quality score by the weighted combination of them, which may introduce contradictory results. First, a pristine benchmark evaluation model is established on the spatial features from the original high-spatial-resolution (HR) panchromatic (PAN) image and the spectral invariant assumption between ideal fused and original multispectral (MS) images. Second, a testing evaluation model for the fused image is founded. Finally, the quality of the fused image is measured based on the distance between the testing and benchmark models. The experimental results demonstrated the superior performance of the proposed method. Furthermore, the proposed method can be generalized to other interesting tasks, such as the nonreference evaluation for pansharpening with missing information and the nonreference evaluation for hyperspectral image fusion. The source code is available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yyxhpkq/MQNR</uri> .