Implementation and comparative quantitative assessment of different multispectral image pansharpening approches

TL;DR

This paper implements and compares various multispectral image pansharpening algorithms, evaluating their effectiveness in enhancing spatial resolution while preserving spectral quality using quantitative metrics.

Contribution

It provides a comprehensive implementation and assessment of state-of-the-art pansharpening methods using MATLAB, with detailed quantitative and visual quality analysis.

Findings

Different algorithms vary in spectral and spatial quality performance.

Quantitative metrics like CC, RMSE, RASE, and Q effectively evaluate pansharpening quality.

The study offers insights into the strengths and limitations of each approach.

Abstract

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.