## 4 Experimental Results
### Experimental Results from Rural Areas  
Figures A1-A4 belongs the Pan sharpening results of the rural test sites (frame F1 from D1 dataset, frames F5 and F6 from D2 and Frame F10 from D3 datasets). Each figure belongs to a representative part from the whole image focusing on rural areas and presents visual comparison different Pan sharpening techniques. The visual comparison of the Pan sharpening methods reveals that spatial resolution of MS images improved significantly in all methods. As for spectral information, parts c, e and h show that the CIELab GS and NNDiffuse methods protect the spectral characteristics better; specifically, for the bands belonging to the visible region. The color-based visual interpretation in vegetated and forest areas in Figures A2-A4 inform us that the Pan-sharpened and original MS images are very similar to each other for GS and CIELab methods. Similar comments can be made on NNDiffuse and CIELab methods in Figure A1. On the other hand, visual comparison of part a with parts d, f, g and i reveals that the remaining four methods were not able to preserve the spectral characteristics of vegetated and forest areas. Particularly, IHS, Ehlers and HCS methods inherited the high frequency impact over vegetated area and could not preserve original spectral/color information for the first test site. In addition, the result of the HCS method is more blurred than the others. The GIHS and--in some cases--the NNDiffuse methods, preserved the color information better than the IHS, Ehlers and HCS; \begin{table}
\begin{tabular}{r l l l} \hline \hline
**Image** & **Location** & **Type** & **Objects** \\ \hline F1 & Istanbul & Rural & Trees, Vegetation, Bare roads, Bare soil \\ F2 & Istanbul & Urban & Buildings, Roads, Squares, Trees, Bare soil \\ F3 & Istanbul & Suburban & Vegetation, Roads and highways, Buildings, Bare soil, Industry buildings, Green fields. \\ F4 & Izmir & Urban & Buildings, Roads, Vegetation, Bare soil \\ F5 & Izmir & Rural & Agricultural fields, Bare roads \\ F6 & Izmir & Rural & Trees, Mountain, Bare roads \\ F7 & Izmir & Suburban & Industry buildings, Agricultural fields, Roads, Bare soil \\ F8 & Aydin & Suburban & Agricultural fields, Roads, Bare roads, Trees, Buildings, Bare soil \\ F9 & Aydin & Urban & Buildings, Roads, Trees, Bare soil, Green fields \\ F10 & Aydin & Rural & Trees, vegetation, agricultural fields, buildings, roads, Bare roads \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The description of ten selected data frames. nevertheless, observable spectral distortion is apparent in their resulting products. The GS method has good performance in the case of vegetation except Figure 14 part e, while results are not satisfactory in pathways and their surroundings. In addition, obvious distortions are apparent in the shadowed areas. Detailed investigation on Figure 15 (e) reveals that there is an obvious distortion in snowy parts of the frame almost in all methods except the proposed CIELab, which resulted in a nearly blue color instead of white snow color. However, CIELab method could be able to preserve the texture and keep the small variances in the color when compared to original MS image. Besides, visual interpretation of the CIELab Pan sharpening results (part c in all figures) demonstrated that use of this color space for Pan sharpening could help to distinguish different tree types and vegetation from each other in the absence of NIR band. The seven quality metrics, which were presented in Table 2, were used for spectral quality assessment of the Pan sharpening results. Numerical results from these metrics for the rural frames (F1, F5, F6 and F10) are presented in Table 14. The metric values were calculated band by band, and the average values of three bands were used for the accuracy assessment procedure. Numerical results of ERGAS, RASE, RMSE and SAM metrics indicated that the proposed CIELab method produced better results than the remaining methods and was followed by the GS method for most of the metrics.