# Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection
## 3 Results
### Further Evaluations  
Regarding the PSSP score, Figure 12 shows the snow occurrence probability curves obtained for three successive Sentinel-2 observation dates (pink curves), with no clouds recorded over the Grandes-Rousses massif: 15 April 2018, 20 April 2018, and 25 April 2018. The figure also shows snow probability curves obtained from all studied segmentation methods using the closest Sentinel-1 images (16, 22 and 28 April 2018). The sentinel-2 curves indicated probabilities of more than 80% of snow above 2000 m. Depending on observation dates and altitude ranges, the probability curves from the segmentation methods illustrated variable agreements with the Sentinel-2 curves, which were considered a reference. For the date of 15 April, we noted a general agreement between most curves for altitudes below 1500 m for which no significant amount of snow was observed, except for the Magenta method (probably sensitive to agricultural wetlands) and the 2 dB method, which overestimated the occurrence of snow. Between 1500 and 2000 m and above 2000 m, the curves of the different segmentation methods could be well differentiated, with a tendency for the unfiltered methods to underestimate snow compared to Sentinel-2. Wet snow was underestimated with the Otsu-based methods (Otsu, OtsuF). While the situation was similar for 20 April, comparisons with 25 April indicated an overestimation of snow by the filtered methods (except for OtsuF, which did the opposite). In the latter case, however, the increased time lag (three days) between the SAR and optical images may have influenced the results. We evaluated the degree of similarity between the different probability curves by computing the Root Mean Square (RMS) values of the differences between the probability curves (Sentinel-1 minus Sentinel-2). The results of these calculations are presented in Figures 13 and 14. Different altitude ranges were chosen as, for the Grandes-Rousses massif, nearly 70% of the pixels had an altitude \(<\)2000 m, 20% had an altitude between 2000 and 2500, 9% had an altitude between 2500 and 3000 m and only 1% had an altitude above 3000 m. For the RMS values and altitudes below 2000 m, errors were smaller for the CV and MF methods, followed by the RF2 method. The errors seemed to be higher with the Otsu method but RMS values for all methods change depending on the specific dates being considered. Considering all altitudes, the method showing the highest similarity to Sentinel-2 was CV, followed by 2 dBF, MF and RF2. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Scores** & **2 dB** & **2 dBF** & **M** & **MF** & **CV** & **RW** & **RF1** & **RF2** & **Otsu** & **OtsuF** \\ \hline Correlation & 0.63 & 0.80 & 0.70 & 0.82 & 0.77 & 0.81 & 0.79 & 0.83 & 0.64 & 0.72 \\ Hamming distance & 0.18 & 0.10 & 0.15 & 0.09 & 0.11 & 0.09 & 0.10 & 0.08 & 0.18 & 0.14 \\ Difference in area (\%) & \(-7.88\) & \(-19.09\) & \(-21.38\) & \(-8.22\) & \(-11.62\) & \(-13.4\) & \(-20.37\) & \(-4.90\) & \(-31.24\) & \(-32.02\) \\ Struct. Similarity (\%) & 50.19 & 84.76 & 62.47 & 85.46 & 78.26 & 85.83 & 84.47 & 85.57 & 59.44 & 81.60 \\ True Detections (\%) & 74.35 & 78.19 & 71.28 & 85.24 & 80.7 & 82.13 & 77.20 & 87.49 & 62.80 & 66.85 \\ False Alarms (\%) & 12.5 & 1.91 & 5.16 & 4.60 & 5.41 & 3.14 & 1.71 & 5.35 & 4.19 & 1.66 \\ Heidke skill scores & 0.63 & 0.79 & 0.68 & 0.82 & 0.77 & 0.81 & 0.78 & 0.83 & 0.61 & 0.69 \\ \hline \end{tabular}
\end{table} 
Table 2: Segmentation scores of the 22 April 2018 SAR image, over the entire 31 TGL tile. The target image was the Sentinel-2 snow product image from 20 April 2018. Figure 12: Probability curves describing the occurrence of snow by altitude at the scale of the Grandes Rousses massif, obtained for three Sentinel-2 successive dates (pink curves), for which no cloud was recorded over the Grandes-Rousses massif. (**a**) 15 April 2018, (**b**) 20 April 2018 and (**c**) 25 April 2018.