A method to improve full-resolution remote sensing pansharpening image quality assessment via feature combination

Pansharpening is an important fusion method in remote sensing image processing, aiming at obtaining a multispectral image with high spatial resolution by fusing a multispectral image of low spatial resolution with a panchromatic image of high spatial resolution. A persistent problem with this approach concerns how the quality of the fused images obtained by different methods can be assessed. We address this by constructing a full-resolution quality assessment method of pansharpened remote sensing image based on the combination of low-level features and deep-level features. For the former, different distribution parameters are estimated based on three types of image features prior. On the other hand, deep-level features are designed by employing network. Finally, the quality scores are obtained by combining the two types of features with random forest for regression. The experimental results showed that, compared with the state-of-the-art methods, the FCBM method in this paper can improve the PLCC metric by 16.2%, the SROCC metric by 25%, and the RMSE metric by 42.9%.