Comparative analysis of activation functions in U-Net for binary water segmentation using Sentinel-2 imagery

Th study examines how different activation functions influence the performance of a U-Net model applied to binary water-body segmentation in Sentinel-2 imagery.Using an identical training setup for each experiment, six nonlinearities-ReLU, Leaky ReLU, ELU, PReLU, Swish and RReLU-are individually substituted into the network while all other parameters remain fixed.Comparative evaluation on a held-out validation set reveals that Leaky ReLU provides the most balanced trade-off between precision and recall, making it the preferred choice for accurate water-mask generation.PReLU offers a similar but slightly lower performance, whereas ELU excels at capturing additional water pixels at the cost of more false positives.The findings highlight the importance of activation-function selection in remote-sensing segmentation tasks and suggest further exploration of advanced nonlinearities and larger, more diverse datasets to enhance generalization.