METHODS FOR IMAGE FUSION QUALITY ASSESSMENT - A REVIEW, COMPARISON AND ANALYSIS

This paper focuses on the evaluation and analysis of seven frequently used image fusion quality assessment methods to see whether, or not, they can provide convincing image quality or similarity measurements. The seven indexes are Mean Bias (MB), Variance Difference (VD), Standard Deviation Difference (SDD), Correlation Coefficient (CC), Spectral Angle Mapper (SAM), Relative Dimensionless Global Error (ERGAS), and Q4 Quality Index (Q4), which were also used in the IEEE GRSS 2006 Data Fusion Contest. Four testing images are generated to evaluate the indexes. Visual comparison and digital classification demonstrate that the four testing images have the same quality for remote sensing applications; however, the seven evaluation methods provide different measurements indicating that the four images have varying qualities. The image fusion quality evaluation by Alparone, et al. (2004) and that by the IEEE GRSS 2006 data fusion contest (Alparone, et al., 2007) are also analyzed. Significant discrepancy between the quantitative measurements, visual comparison and final ranking has been found in both evaluations. The inconsistency between the visual evaluations and quantitative analyses in the above three cases demonstrate that the seven quantitative indicators cannot provide reliable measurements for quality assessment of remote sensing images.