Rank Learning Based Full-Resolution Quality Evaluation Method for Pansharpened Images

Full-resolution quality evaluation model for pansharpened images is significant for remote sensing applications, yet presents a challenge of the absence of reference compared with the reduced-resolution approach. To predict the image quality accurately, it is necessary to consider the distortion during the pansharpening process. Based on an observation that the quality of pairwise images can more easily be ranked, we propose a rank learning based full-resolution quality evaluation method for pansharpened images. Our approach begins with the synthesizing of ranked distortion images in spatial and spectral domains. Then, we develop a pansharpening distortion-perceiving model. This model employs spatial and spectral Siamese networks to perceive distortions and applies a pair-wise learning strategy for ranked images. Consequently, we establish a distortion-guided full-resolution quality evaluation framework for pansharpening. This framework integrates the spatial and spectral distortion-perceiving network and is enhanced with a dimension alignment module and a discrepancy Rrpresentation module, enabling effective distortion extraction among high-resolution multispectral, panchromatic, and low-resolution multispectral images. We conducted a series of experiments on a large-scale public pansharpened database. The experimental results demonstrate the effectiveness of our proposed approach.