The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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question: how can the GLP-CBD QuickBird fusion result 
published in the contest outcome paper in 2007 appear 
significantly better than that submitted to the contest in 2006? 
UNB fusion result (0.7m) 
UNB fusion result (0.7m) 
Original Pan (0.7m) 
Original Pan (0.7m) 
Figure 4. Subsets of the QuickBird fusion results of UNB-Pansharp submitted to the IEEE GRSS 2006 Data Fusion Contest (UNB- 
Pansharp can produce fusion results either with or without feature enhancement. The fusion results with feature enhancement were 
submitted to the contest. All images in this figure are displayed under the same image stretching condition.) 
The inconsistency and irregularity in the evaluation of IEEE 
GRSS 2006 Data Fusion Contest also raised the question on the 
capacity of the seven quantitative indicators (MB, VD, SDD, 
CC, SAM, ERGAS, and Q4) for quality measurements between 
images. 6 
6. CONCLUSIONS 
This paper analyzed and evaluated three cases of image quality 
comparisons using visual and quantitative methods. The three 
cases are (1) visual and quantitative analysis of the four testing 
images generated for this study; (2) review and analysis of the 
fusion quality evaluation by Alparone et al.,(2004), which 
received the 2004 IEEE Geoscience and Remote Sensing Letter 
Best Paper Award (Alparone et ah, 2007); and (3) review and 
analysis of the evaluation of the IEEE GRSS 2006 data fusion 
contest. The quantitative methods evaluated are the seven 
frequently used indicators—Mean Bias (MB), Variance 
Difference (VD), Standard Deviation Difference (SDD), 
Correlation Coefficient (CC), Spectral Angle Mapper (SAM), 
Relative Dimensionless Global Error (ERGAS); Q4 Quality 
Index (Q4)—which are also the quantitative measures of the 
IEEE GRSS 2006 Data Fusion Contest. 
In the visual and quantitative analysis of the four testing images 
generated for this study, it was found: 
• The four testing images generated through mean 
shifting and/or histogram stretching provide the same 
visualization and classification results under the same 
display and classification conditions. This demonstrates 
that mean shifting and histogram stretching (within the