Implementation and comparative quantitative assessment of different multispectral image pansharpening approches
This work addresses the challenge of improving image resolution for remote sensing applications, but it is incremental as it focuses on comparative evaluation of existing methods.
The paper implemented and compared several state-of-the-art pansharpening algorithms to enhance spatial resolution while preserving spectral quality in multispectral remote sensing images, using quantitative metrics like correlation coefficient and root mean square error for assessment.
In remote sensing, images acquired by various earth observation satellites tend to have either a high spatial and low spectral resolution or vice versa. Pansharpening is a technique which aims to improve spatial resolution of multispectral image. The challenges involve in the pansharpening are not only to improve the spatial resolution but also to preserve spectral quality of the multispectral image. In this paper, various pansharpening algorithms are discussed and classified based on approaches they have adopted. Using MATLAB image processing toolbox, several state-of-art pan-sharpening algorithms are implemented. Quality of pansharpened images are assessed visually and quantitatively. Correlation coefficient (CC), Root mean square error (RMSE), Relative average spectral error (RASE) and Universal quality index (Q) indices are used to easure spectral quality while to spatial-CC (SCC) quantitative parameter is used for spatial quality measurement. Finally, the paper is concluded with useful remarks.