*4.2. Performing the Algorithm and Accuracy Assessment*

To measure the performance of Pan sharpening results using the metrics that were presented in Section 3.3, the Wald protocol was used, due to lack of reference a high-resolution MS image [53]. According to Wald protocol, all Pan sharpening experiments were done using degraded datasets, which are produced by decreasing spatial resolution of the original dataset (reduce MS and Pan, respectively, to 8 m and 2 m). The Pan sharpening results obtained that way, can be compared with the original MS images for an accuracy assessment procedure. In this paper, six Pan sharpening methods and eleven accuracy indexes are evaluated to perform a comparative accuracy assessment of the proposed method. The numerical results of the accuracy indexes are presented in Tables A1–A6. The visuals belonging to Pan sharpening results of ten frames are presented in Figures A1–A10. In each figure, parts a and b are the original Pléiades MS and Pan images, respectively. Parts c, d and e are the Pan sharpened results from the CIElab, GIHS and GS methods, respectively. The Pan sharpened images from the HCS, IHS, NNDiffuse and Ehlers methods are shown in parts f–i, respectively.

#### *4.3. 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; nevertheless, observable spectral distortion is apparent in their resulting products. The GS method has

good performance in the case of vegetation except Figure A1 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 A3 (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 A1. 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. Metric-based results were in line with the visual interpretation. The CIELab method also provided the highest accuracies according to the QAVG and PSNR metrics. In addition, the proposed method provides the value 1 for the SSIM metric, which is the best possible value. Moreover, the IHS method provides worst results for all quality indexes, with respect to Table A1. Lastly, Ehlers, GIHS and HCS methods provide lower accuracies in some cases. This unstable manner of these methods across different scenes is another problem that should be considered.

To assess the spatial quality of Pan sharpened images, the CC, the Zhou index, Sobel RMSE and spatial ERGAS indexes that are presented in Section 3.3, were calculated by comparing the Pan image and the intensity component of the Pan sharpened images. Numerical results of these metrics are presented in Table A2. According to comparative evaluation, the Pan sharpened image from the proposed CIELab method provided the highest spatial CC and Zhou values and lowest SRMSE and SP ERGAS values. These results indicate that the proposed method has the best spatial performance among all methods tested. Ehlers, HIS and HCS methods provided the lowest spatial performances according to the values presented in Table A2.

As a result, the proposed CIELab method provided the best performance for the rural scenes based on the visual interpretation and spectral and spatial quality metrics results.
