# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 5 Results
### Baseline Comparisons  
We compare our predicted changes with unsupervised change detection, as described in Section 4.3. Results on Table 3 show that most of the baselines are not sensitive enough for detecting any changes in the coconut plantations. Our method detects changed vs. unchanged coconut pixels with an accuracy of 88.6%, and a precision and recall of 75% and 90%, respectively. The baseline methods detect any type of change, but we can use validation points that contain coconut to evaluate their performance on coconut plantation areas (See Table 4). \(k\)-means, CVA and KPCA all obtain a recall of 27.3%. GAN, DSFA and DCVA did not predict any correct changes in our validation areas. KPCA has an overall accuracy of 80.5%, which is however largely due to the fact that it predicts almost no changes at all. Furthermore, we visually analysed the different damaged areas. Results are shown in Figure 10. We concentrate our validation efforts on areas with strong damages (top) \begin{table}
\begin{tabular}{l r r r r r r r r r} \hline \hline  & **Ours** & **GAN** & \(k\)**-Means** & **DSFA** & **DCVA** & **CVA** & **KPCA** \\ \hline Accuracy & 88.6 & 56.1 & 36.6 & 73.2 & 56.1 & 61.0 & 80.5 \\ Recall & 90.0 & 0.0 & 27.3 & 0.0 & 0.0 & 27.3 & 27.3 \\ Precision & 75.0 & 0.0 & 14.3 & 0.0 & 0.0 & 27.3 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Validation results in %. The metrics were computed only at points with true landcover coconut, since unsupervised baselines are unable to detect coconut and also flag changes in the background, contrary to our method. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Ours**} & \multicolumn{3}{c}{**GAN**} & \multicolumn{3}{c}{**\(k\)-Means**} & \multicolumn{3}{c}{**DSFA**} & \multicolumn{3}{c}{**DCVA**} & \multicolumn{3}{c}{**CVA**} & \multicolumn{3}{c}{**KPCA**} \\  & **C** & **N** & **U** & **C** & **N** & **C** & **N** & **C** & **N** & **C** & **N** & **C** & **N** & **C** & **N** \\ \hline \multicolumn{12}{c}{_Coconut_} \\ \hline C (11) & 9 & 1 & 1 & 0 & 11 & 3 & 8 & 0 & 11 & 0 & 11 & 3 & 8 & 3 & 8 \\ N (30) & 3 & 22 & 5 & 7 & 23 & 18 & 12 & 0 & 30 & 7 & 23 & 8 & 22 & 0 & 30 \\ \hline \multicolumn{12}{c}{_Background_} \\ \hline (46) & 0 & 43 & 3 & 0 & 46 & 29 & 17 & 0 & 46 & 10 & 36 & 14 & 32 & 4 & 42 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrices for 87 validation points. For the comparison, our predicted classes new, unchanged and not coconut had to be merged into a single background class to match the outputs of pure change detection. C: Change, N: No_change, U: Uncertain. Figure 9: Comparison of density estimations and high resolution reference data in Samar, Philippines. Images before and after were sensed on 11 December 2018 and 3 February 2021 respectively. and mixed change states (bottom). For reference, we also include the Sentinel-2 median composite that served as input for the baselines. DSFA shows no clear indication of changes in coconut plantations, in either of the analysed locations. It does however flag some changes in vegetation and soil areas. As expected, the method homes in on the strongest changes between the two images, however it does not seem to be able to detect coconut damages, which are visually more subtle than other changes. \(k\)-means, on the other hand, flags many more changes. It appears that strong, apparent changes are detected much better than with DSFA. This could be partially due to the fact that \(k\)-means uses a neighbourhood of pixels to cluster each pixel instead of single pixels as DSFA. Some of the predicted changes by \(k\)-means in the top image are in areas that were reported as damaged coconut areas. It is unclear however which type of change this could be representing as in the composite images there is no clear indication of change. S2-cGAN, similar to \(k\)-means, detects some changes in areas that are heavily affected according to our prediction. It seems to have fewer false positives than DSFA and \(k\)-means.