# Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images
## 3 Results
### Scenario 3: Runway Area  
Here, we used two Planet images at 27 July and 5 August and one WV image at 26 July to generate a prediction at 5 August. Due to lack of a WV image at 5 August, we used the WV image at 3 August as the ground truth. Figure 11 illustrates the prediction scenario. The prediction performance metrics  
Figure 10: SSIM maps of the Field scenario using different methods. (a–d) are the maps for STARFM, FSDAF, HCM, and bicubic, respectively.  
Figure 9: Comparison of different fused images with the ground truth (WV) and the low resolution Planet image at the prediction time. Field area.  
are summarized in Table 7. In terms of PSNR, all three fusion algorithms improved over the Planet image by 3 dBs. In terms of SSIM, the improvement is close to 10%. Other metrics show a similar trend as the above metrics. In terms of subjective evaluation, one can see from Figure 12 that the fusion algorithms can recover the fine details whereas the Planet image missed quite a few of the detailed features inside the red bounding box.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**RGB**} & \multicolumn{6}{c}{**R**} \\ \hline  & \multicolumn{3}{c}{**Runway**} & \multicolumn{6}{c}{**Runway**} \\ \hline  & \multicolumn{3}{c}{**Runway**} & \multicolumn{6}{c}{**Runway**} \\ \hline  & STARFM & FSDAF & HCM & Bicubic & & & STARFM & FSDAF & HCM & Bicubic \\ RMSE & 0.0327 & **0.0316** & 0.0329 & 0.0457 & RMSE & 0.0344 & **0.0333** & 0.0348 & 0.0489 \\ PSNR & 29.7128 & **30.0081** & 29.6460 & 26.8029 & PSNR & 29.2637 & **29.5449** & 29.1655 & 26.2078 \\ AD & 0.0231 & **0.0218** & 0.0231 & 0.0304 & AD & 0.0255 & **0.0242** & 0.0256 & 0.0336 \\ CC & 0.5300 & **0.5301** & 0.5293 & 0.5031 & CC & 0.9534 & **0.9534** & 0.9517 & 0.8967 \\ SAM & 1.3649 & 1.3469 & **1.3208** & 1.8684 & SSIM & **0.8451** & 0.8451 & 0.8417 & 0.7209 \\ SSIM & **0.8896** & 0.8892 & 0.8862 & 0.7903 & ERGAS & 14.0951 & 13.6461 & **13.2019** & 20.0384 \\ ERGAS & 12.8138 & 12.3881 & **12.0533** & 17.9663 & Q2N & 0.6589 & 0.6783 & **0.7852** & 0.5326 \\ Q2N & 0.7932 & 0.8020 & **0.8081** & 0.6833 & & & & & \\ \multicolumn{3}{c}{**(a)** Metrics for all 3 bands} & \multicolumn{6}{c}{**(b)** Metrics for Red band} \\ \hline  & \multicolumn{3}{c}{**G**} & \multicolumn{6}{c}{**B**} \\ \hline  & \multicolumn{3}{c}{**Runway**} & \multicolumn{6}{c}{**Runway**} \\ \hline  & STARFM & FSDAF & HCM & Bicubic & & STARFM & FSDAF & HCM & Bicubic \\ RMSE & 0.0323 & **0.0313** & 0.0325 & 0.0463 & RMSE & 0.0312 & **0.0301** & 0.0315 & 0.0415 \\ PSNR & 29.8063 & **30.0904** & 29.7745 & 26.6800 & PSNR & 30.1111 & **30.4357** & 30.0453 & 27.6414 \\ AD & 0.0227 & **0.0214** & 0.0226 & 0.0304 & AD & 0.0211 & **0.0197** & 0.0211 & 0.0271 \\ CC & 0.9566 & **0.9568** & 0.9557 & 0.9023 & CC & 0.9528 & **0.9531** & 0.9513 & 0.9088 \\ SSIM & **0.8758** & 0.8757 & 0.8737 & 0.7656 & SSIM & **0.8908** & 0.8894 & 0.8856 & 0.8023 \\ ERGAS & 12.8810 & 12.4665 & **12.0869** & 18.4612 & ERGAS & 11.3131 & 10.8981 & **10.7453** & 15.0338 \\ Q2N & 0.6759 & 0.7228 & **0.8018** & 0.5914 & Q2N & 0.6578 & 0.6973 & **0.7830** & 0.5854 \\ \multicolumn{3}{c}{**(c)** Metrics for Green band} & \multicolumn{6}{c}{**(d)** Metrics for Blue band} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of prediction results of using Planet and WV images for the scenario depicted in Figure 11.  
Figure 11: Two Planets images at 27 July and 5 August and one WV image at 26 July are fused to generate a prediction. The prediction is then compared to a WV image collected on 3 August 2017.  
Figure 12: Comparison of different fused images with the ground truth (WV) and the low resolution Planet image at the prediction time. Runway area.  
Figure 13 shows the SSIM maps, which further corroborate that the HCM, FSDAF, and STARFM all have better performance than bicubic.