Deep semantic fusion of sentinel-1 and sentinel-2 snow products for snow monitoring in mountainous regions

Snow holds a significant importance as a fundamental environmental factor in multiple domains. Obtaining accurate ground truth data for snow mapping in mountainous areas presents a significant challenge. To address this issue, this paper presents a deep semantic learning framework for the segmentation of Sentinel-1 images for wet snow detection in mountainous areas. Firstly, we propose to create a deep leaning database based on snow products derived from Sentinel-1 and Sentinel-2 data. Afterward, we introduce a deep convolutional neural network called ReXcepUnet, which combines the U-Net architecture and the powerful Xception backbone. Finally, the proposed framework has been successfully applied to monitor wet snow in the Mont Blanc massif, yielding high accuracy results. The ReXcepUnet model demonstrates a good performance in wet snow detection, particularly in high-relief regions like the Mont Blanc massif.