# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
## 3 Results and Discussion
### Usability Verification of the Sharpened Image  
Supervised classification was performed to verify the usability of the sharpened image produced by the method proposed in this study, and the supervised classification technique applied a support vector machine (SVM), one of the machine learning techniques. For SVM classification, 16 bands produced by the proposed method and 8 VNIR bands produced by the existing sharpening method were applied, and quantitative and qualitative comparison accuracy evaluations were performed. Figure 7: Comparison of spectral information between the original image and sharpened image (SWIR bands 8, 4, and 1 are shown as RGB): (**a**) comparison of spectral information of vegetation pixels in site 1; (**b**) comparison of spectral information of building roof pixels in site 1; (**c**) comparison of spectral information of vegetation pixels in site 2; (**d**) comparison of spectral information of building roof pixels in site 2. Training and reference data for classification were acquired from images, and classification classes were selected as building roofs, roads, vegetation, bare soil, water bodies, and cultivation facilities. In particular, the building roof was classified into three types according to color. Table 3 shows the number of pixels of training and reference data, and Figure 8 shows the results of SVM classification. The classification results using 16 bands and 8 bands were quantitatively and qualitatively compared and evaluated. As a result of qualitative evaluation through visual reading, it was read that the classification result to which SWIR was additionally applied showed improved performance for both study areas. In particular, it was confirmed that the classification accuracy of buildings, roads, and cultivation facilities, which are artificial structures, is improved. The quantitative evaluation results showed similar results to the qualitative evaluation. Table 4 shows the quantitative evaluation results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Site 1**} & \multicolumn{2}{c}{**Site 2**} \\ \cline{2-5}  & **Training Data** & **Reference Data** & **Training Data** & **Reference Data** \\ \hline
**Blue Roof** & 521 & 1017 & 588 & 1045 \\ \hline
**Green Roof** & 568 & 1091 & 556 & 1073 \\ \hline
**Black Roof** & 575 & 1074 & 514 & 1069 \\ \hline
**Road** & 522 & 1127 & 545 & 1120 \\ \hline
**Vegetation** & 563 & 1036 & 530 & 1128 \\ \hline
**Bare Soil** & 521 & 1101 & 584 & 1095 \\ \hline
**Water Bodies** & 525 & 1024 & 576 & 1063 \\ \hline
**Cultivation Facility** & 541 & 1107 & 534 & 1023 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of pixels in the training and reference data. Figure 8: Comparison of classification results: (**a**) RGB image of site 1, (**b**) classification result using only VNIR bands, (**c**) classification result using VNIR and SWIR bands, (**d**) RGB image of site 2, (**e**) classification result using only VNIR bands, (**f**) classification result using VNIR and SWIR bands. The classification accuracy for site 1 using 16 bands was 95.29%, and the classification result using 8 bands was 88.56%. Thus, it was confirmed that the classification accuracy is improved when SWIR bands are additionally applied to classification. In addition, as a result of classification for each class, the classification accuracy of artificial structures such as building roofs, roads, and cultivation facilities was improved compared to that of natural features. For site 2, the classification results using 16 bands and 8 bands were calculated with 91.73% and 88.89% accuracy, respectively. Site 2 also showed high accuracy when classification was performed using 16 bands. When comparing each class, it was confirmed that the classification accuracy of the blue-roof buildings, black-roof buildings, and cultivation facilities among artificial structures was improved. However, the accuracy for the green-roof buildings decreased.