R2UNet for melt pond detection

摘要: The massive shift in temperatures in the Arctic region has caused the increased Albedo effect as higher amount of solar energy is absorbed in the darker surface due to melting ice and snow. This continuous regional warming results in further melting of glaciers and loss of sea ice. Arctic melt ponds are important indicators of Arctic climate change. High-resolution aerial photographs are invaluable for identifying different sea ice features and are great source for validating, tuning, and improving climate models. Due to the complex shapes and unpredictable boundaries of melt ponds, it is extremely tedious, taxing, and time-consuming to manually analyze these remote sensing data that lead to the need for automatizing the technique. Deep learning is a powerful tool for semantic segmentation, and one of the most popular deep learning architectures for feature cascading and effective pixel classification is the UNet …