# Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection
## 4 Conclusions  
Wet snow cover monitoring is one of the case use of Sentinel-1 satellite images. Due to their high spatial resolution and short revisit time, they are ideal for tracking the dynamics of wet snow. SAR measurements can detect wet snow in almost all weather conditions, making them a valuable complement to optical measurements, especially in cloudy situations. This study evaluated the possibility of using segmentation methods for detecting wet snow in Sentinel-1 SAR images and their abilities to track seasonal changes. Several segmentation methods from different families were used and applied to the Sentinel-1 SAR image time series over the 2017-2018 period for a steep mountain area in the French Alps. The study compared wet snow segmentation results, optical snow masks, and information from Meteo-France forecast bulletins. The results showed that the segmentation methods effectively identified wet snow in the study area and indicated the significance of filtering images before segmentation. Additionally, we show that the PSSP score is valuable for exploring segmentation results by comparing snow probability curves on a mountain \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Scores** & **2 dB** & **2 dBF** & **M** & **MF** & **CV** & **RW** & **RF1** & **RF2** & **Otsu** & **OtsuF** \\ \hline Correlation & 0.50 & 0.77 & 0.60 & 0.71 & 0.56 & 0.77 & 0.78 & 0.72 & 0.51 & 0.72 \\ Hamming distance & 0.20 & 0.08 & 0.13 & 0.11 & 0.22 & 0.08 & 0.08 & 0.11 & 0.21 & 0.10 \\ Difference in area (\%) & 62.90 & \(-\)10.14 & 11.72 & 33.95 & 151.53 & 1.41 & \(-\)14.73 & 31.56 & 94.43 & \(-\)29.01 \\ Struct. Similarity (\%) & 55.49 & 90.16 & 66.84 & 86.88 & 66.29 & 90.63 & 90.18 & 85.10 & 58.93 & 89.29 \\ True Detections (\%) & 74.25 & 78.18 & 70.79 & 87.91 & 86.33 & 83.10 & 76.31 & 89.11 & 69.95 & 65.83 \\ False Alarms (\%) & 17.89 & 3.17 & 8.11 & 10.53 & 21.97 & 4.80 & 2.47 & 11.30 & 14.69 & 6.89 \\ Heidke skill scores & 0.47 & 0.77 & 0.59 & 0.69 & 0.52 & 0.77 & 0.77 & 0.70 & 0.48 & 0.70 \\ Runtime (s) & 2.69 & 9.80 & 44.76 & 67.63 & 1893.4 & 2266.8 & 72.71 & 118.27 & 43.85 & 40.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Segmentation scores of the Melt season (from 10 April to 17 June 2018) over the entire 31 TGL tile. The target images are Sentinel-2 snow products (we allowed up to 5 days of difference in observation dates). massif scale. We also show that some segmentation techniques overestimate snow at lower altitudes while others underestimate snow at higher altitudes. The PSSP score could help guide the calibration of segmentation methods and could fine-tune the parameters to obtain the best possible segmentation results. It is important to note that some scores used in this study might favour techniques that create a smoother wet snow mask rather than methods that retain some variability in the wet snow area contours. Therefore, it is essential to maintain a close connection between the segmentation techniques discussed in this study and the metrics employed to evaluate effectiveness. Our study also questions the need to use existing distances or to develop new scores to better discriminate between methods that preserve the variability of wet snow zone contours. Some techniques used in this study (such as CV and RW) have great potential for image segmentation but need further investigation, particularly with a view to optimizing the computation time and calibrating the method better regarding initial conditions, parameters, implementation and possible generalizations. Additional work is in progress to explore the possibilities of the Chan-Vese and Random Walker methods in more detail. It is essential to consider the area and period studied and the evaluation methods used when analyzing the results. To gain a more comprehensive understanding of the findings, conducting tests over an extended period, ideally spanning several winter seasons, is necessary. Research design, F.K., G.J. ; Data analysis, A.G., G.J., F.K. ; Data Preprocessing: P.D., F.K. ; Result analysis, A.G., F.K., G.J.