# Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection
## 3 Results  
### Focus on a Situation  
Let us first look at the results for 22 April 2018, which corresponded to a generalized melting period. On that day, only 5 in-situ measurement stations, belonging to the Meteo-France station network and located above 2800 m, measured a temperature below 5 \({}^{\circ}\)C  
Figure 10: Flowchart of all processing of Sentinel-1 images with segmentation methods and evaluation of wet snow products.  
at mid-day. We, therefore, considered that all the areas located below 3000 m (which represented 98.2% of our study area) were associated with wet snow. We used the binary total snow product from Sentinel-2, as of 20 April 2018, to compare the segmentation results. To better evaluate the results of the different segmentation methods, let us zoom in on the Belledonne Massif area, although the segmentation was performed on the entire 31 TGL tile. Figure 11 shows the segmentation results of the CV, RW, 2 dB, 2 dBF, M, RF1 and RF2 methods (see Figure 6 and the section 2 for a detailed description of segmentation methods) overlaid on the Sentinel2 total snow extent. On these maps, the grey colour is for situations where no snow detection was performed by either Sentinel-11 or Sentinel-2, the blue colour is for cases where snow and wet snow were detected, red colours are for a situation where snow was detected by Sentinel-2 but not detected by Sentinel-1, and, finally, the yellow colour is for cases where wet snow was detected by Sentinel-1, and Sentinel-2 indicated snow free pixels.  
The snow zone had many apparent false alarm zones (in yellow). These areas appeared on shaded northern slopes where there was snow, which did not appear on the optical snow mask. We double-checked this by looking at high-resolution optical images, to ensure it was not a mistake in the segmentation methods. We also noted that methods without prior filtering (M, 2 dB), or low image filtering (CV) prior to segmentation, produced noisier wet snow binary masks than the other methods. The degree of filtering of the Chan-Vese method was dependent on the value of the parameter \(\mu\). However, these methods did a better job of preserving the contours of snow areas with maximum detail. All the methods tended to under-detect snow edges. The Random Forest and Random Walker methods were the ones that were the closest to the observed mask of Sentinel-2, but at the cost of significant smoothing of the contours and loss of details within the snow-covered area.  
In Table 2, scores for the whole 31 TGL tile are given. One can notice that the methods which filtered the most (RW) obtained the best structure similarity scores, which was highly correlated to the amount of noise remaining on the segmented image (see Section 2.4.2). The methods combining a reasonable structure similarity rate and good detection obtained the best correlation scores, Hamming distance and Heidke Skill Scores.  
Figure 11: Example of segmentation outputs over the massif of Belledonne using SAR data from 22 April 2018. Results were superposed with Sentinel-2 snow mask (20 April 2018).