# Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images
## 3 Results
### Scenario 2: Field Area  
In this scenario, we used two Planet images collected at 19 July and 27 July and one WV image at 18 July to jointly predict a high resolution image at 27 July. Because there is no high resolution WV image at 27 July, we used the WV image at 26 July as the ground truth for generating the performance \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**RGB**} & \multicolumn{3}{c}{**R**} \\ \hline  & \multicolumn{3}{c}{**Canal**} & \multicolumn{3}{c}{**Canal**} & \multicolumn{3}{c}{**Canal**} \\ \hline  & \multicolumn{3}{c}{**Canal**} & \multicolumn{3}{c}{**Canal**} & \multicolumn{3}{c}{**} \\ \hline  & \multicolumn{3}{c}{**STARFM**} & FSDAF & HCM & Bicubic & & \multicolumn{3}{c}{**STARFM**} & FSDAF & HCM & Bicubic \\ RMSE & 0.0377 & 0.0373 & **0.0373** & 0.0403 & RMSE & 0.0402 & 0.0403 & **0.0388** & 0.0443 \\ PSNR & 28.4721 & 28.5595 & **28.5760** & 27.9017 & PSNR & 27.9065 & 27.8942 & **28.2132** & 27.0623 \\ AD & 0.0221 & 0.0222 & **0.0221** & 0.0259 & AD & **0.0241** & 0.0245 & 0.0242 & 0.0303 \\ CC & 0.4384 & **0.4411** & 0.4368 & 0.4229 & CC & 0.8499 & 0.8487 & **0.8599** & 0.8199 \\ SAM & 2.1033 & **2.0274** & 2.3677 & 2.3576 & SSIM & **0.8230** & 0.8200 & 0.8138 & 0.6687 \\ SSIM & 0.8491 & **0.8493** & 0.8350 & 0.7366 & ERGAS & 16.7615 & **16.9272** & 16.2325 & 18.5180 \\ ERGAS & 15.2969 & 15.2785 & **15.0919** & 16.4045 & Q2N & 0.7844 & **0.7806** & 0.7906 & 0.6755 \\ Q2N & 0.7795 & **0.7814** & 0.7797 & 0.6737 & & & & & \\ \multicolumn{3}{c}{**(a) Metrics for all 3 bands**} & \multicolumn{3}{c}{**(b) Metrics for Red band**} \\ \hline  & \multicolumn{3}{c}{**G**} & \multicolumn{3}{c}{**B**} \\ \hline  & \multicolumn{3}{c}{**Canal**} & \multicolumn{3}{c}{**Canal**} \\ \hline  & \multicolumn{3}{c}{**STARFM**} & FSDAF & HCM & Bicubic & & \multicolumn{3}{c}{**STARFM**} & FSDAF & HCM & Bicubic \\ RMSE & 0.0374 & 0.0372 & **0.0363** & 0.0396 & RMSE & 0.0353 & **0.0342** & 0.0366 & 0.0364 \\ PSNR & 28.5357 & 28.5890 & **28.8083** & 28.0401 & PSNR & 29.0499 & **29.3102** & 28.7308 & 28.7733 \\ AD & 0.0220 & 0.0222 & **0.0208** & 0.0255 & AD & 0.0204 & **0.0201** & 0.0214 & 0.0220 \\ CC & 0.8089 & 0.8104 & **0.8206** & 0.7836 & CC & 0.7692 & **0.7766** & 0.7567 & 0.7403 \\ SSIM & **0.8443** & 0.8429 & 0.8380 & 0.7148 & SSIM & **0.8448** & 0.8443 & 0.8178 & 0.7413 \\ ERGAS & 15.3994 & 15.4212 & **14.9527** & 16.3709 & ERGAS & 13.5607 & **13.2648** & 14.0079 & 14.0142 \\ Q2N & 0.7592 & 0.7546 & **0.7811** & 0.6686 & Q2N & 0.7237 & 0.7177 & **0.7284** & 0.6468 \\ \multicolumn{3}{c}{**(c) Metrics for Green band**} & \multicolumn{3}{c}{**(d) Metrics for Blue band**} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of prediction results of using Planet and WV images for the scenario depicted in Figure 5. Bold characters indicate the best performing method for that row. Figure 7: SSIM (Structural Similarity) maps of the Canal scenario using different methods. (**a–d**) are the maps for STARFM, FSDAF, HCM, and bicubic, respectively. metrics. The prediction scenario is illustrated in Figure 8. From Table 6, one can see that the three fusion algorithms have improved the PSNRs by about 3 dBs over the bicubic interpolated Planet image. The SSIM values have also been improved by close to 50%. Other metrics have all been improved over the bicubic outputs. By closely inspecting the various images in Figure 9, the fused images can recover more fine details, which were blurred and hidden in the original Planet image at the prediction time. Figure 10 shows the SSIM maps of different methods. We can see that the improvement over bicubic is quite significant.