# Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection
## 3 Results
### Further Evaluations
All methods detected wet snow at low altitudes. These were cases of frozen ground leading to a wet snow detection error [43]. If we look at cumulative scores over the melt period (see Table 3), the RF1 method did better than the tested methods. It had a much lower false alarm rate, in particular. Methods with no or weak filtering had the highest false alarm rates. On the correlation criterion, several methods performed well with correlations above 0.7 (2 dBF, MF, RW, RF1, RF2, OtsuF). Figure 16: Altitude-Time diagram over the Belledonne massif with the normalized percentage of wet snow-covered pixels by class of elevation and time for all Sentinel-1 ascending images. Results are given for sourthern orientations. Results are given for 4 segmentation methods: 2 dBF, RF1, MF and RW. When available, the altitude of Snow–Rain lines (or range of altitudes) are displayed as red square symbols. The snow lines, as well as the isotherm 0 line, are displayed as black and red curves, respectively. These estimates were taken from Meteo-France forecast bulletins (see Figure 4). Figure 17: Same as Figure 16 but for northern orientations. The Chan-Vese method, as tested in this study, had two limitations: in cases of low snow coverage, the chosen setting tended to over-segment the scene, resulting in excess detection at the end of the season. One could correct this problem by adjusting the parameters \(\
u\) and \(\lambda_{i}\). The second issue concerned the possible inversion of the segmented classes since the method decomposed two classes without labeling them, which could lead to confusion between snow-covered and snow-free areas for specific dates. These problems are highlighted in Figure 15, wherein can be observed a degradation of the CV method performances (in terms of correlation, Hamming distance and Heidke Skill Score) at the end of May 2018. A class inversion occurred with the CV method on 16 January, leading to a relative peak in the Hamming distance (decrease in correlation and Heidke Skill Score), unlike the other segmentation methods. The methods which reduced noise effectively consistently achieved the best scores. If we focus on the top 3 methods, we notice that their scores were very close, although they were very different. The level of smoothing (much higher for the Random Walker method) had little influence on the correlation rate, as long as the noise was reduced. It is worth noting that methods such as RF2, MF, and 2 dBF were of particular interest because they saved the contours of the wet snow areas as much as possible. Regarding execution time, the RW and CV methods were costly in terms of computation time, but they offered different degrees of freedom and ways of improvement. It is worth noting that no optimization was undertaken during this study and that these methods could be optimized numerically. Further optimization work might lead to different conclusions. We selected a range of segmentation techniques that complemented each other and were straightforward to implement. We did not aim to provide an all-encompassing analysis and comparison of every segmentation method available. The proposed comparative study can still be helpful for users in selecting the most suitable option for their specific applications.