*4.4. Experimental Results from Urban Areas*

Figure A5 through Figure A7 belongs to the Pan sharpening results of the urban test sites (frames F2, F4 and F9 from D1, D2 and D3 datasets respectively). Each figure belongs to a representative part from the whole image focusing on the buildings and roads, and presents visual comparison between different Pan sharpening techniques on the differently sized and oriented buildings and roads in the urban areas.

Visual comparison results of urban areas revealed that all the Pan sharpened images inherited the high spatial information from the Pan image, and likewise, the results of rural areas. Roads and buildings could be better identified in all Pan sharpened images compared to original MS image. As for spectral information, Figure A5 c,e,h, informed us that CIELab, GS and NNDiffuse methods preserved the spectral characteristics and color information in urban areas. In particular, the color information from the buildings with brick rooves are similar to the original MS image. Visual comparison of Figure A5 part a with parts d, f, g and i illustrated that of IHS, HCS, GIHS and Ehlers methods are not able to preserve the original spectral characteristics of buildings as well as the other three approaches did. In particular, Ehlers, HCS and IHS methods provided blurred and smoggy results with faded and paled colors. Parts g and I from Figures A6 and A7 support that HIS and Ehlers methods provide worst visual results among all methods tested. Part e in Figures A6 and A7 reveals the weakest side of GS method; that is, the poor performance in the Pan sharpening of white tones. White colors tend to seem blueish in results of this method. It is obvious from part f in Figures A6 and A7 that the HCS method provided the most blurred result. GIHS and NNDiffuse methods have acceptable results in comparison with the results from other methods (except CIELab method). Detailed investigation of Figures A6 and A7, parts d and h, prove that NNDiffuse method produces distortion in the shadowed areas and GIHS method has poor performance in vegetated areas and trees. Visual interpretation of Figure A5(c), Figure A6(c) and Figure A7(c) reveal the fact that the proposed CIELab method protected spectral properties of original MS image more than the other methods.

Numerical results of spectral quality assessment of Pan sharpened images belonging to the urban test sites (F2, F4 and F9) are presented in Table A3. Metric values demonstrated that the CIELab method provided the most promising results among all Pan sharpening methods used in this research. This method presented the lowest values for the ERGAS, RASE, RMSE and SAM metrics and highest values for QAVG, PSNR and SSIM metrics (again, the highest possible value obtained for SSIM). HCS and IHS methods provided the worst results for most of the metrics. Once again, the second performance rank for spectral quality was obtained by GS method in most of the metrics.

Table A4 presents the spatial quality metrics results that were calculated from Pan image and the intensity component of Pan sharpened images for the urban test sites. Similar to the rural test sites, the proposed CIELab method provided the highest CC and Zhou values alongside of lowest SRMSE and SP ERGAS values for urban images, which demonstrated the high spatial quality. In particular, there is great gap between the numeric results of SRMSE and SP ERGAS indexes presented with CIELab method and other methods. Ehlers, IHS and HCS methods acted as the worst methods in the case of spatial indexes, which is consistent with the visual results. Consequently, the proposed CIELab method provided the best performance for the urban test sites (frames F2, F4 and F9) as well, based on the visual interpretation and spectral/spatial quality metrics.
