Image segmentation with U-net

I am trying to understand if Semantic segmentation with U-NET. Are we training kernels to extract features or are we training a fully connected layer at the end? Or both? If so, how are we training them? how are we using the loss function to train them? Because based on this picture, i don't think there is a need for a fully connected layer at the end. If we just train our kernels, U-net will just do the image segmentation. I couldn't find any resource for how to train unet model for image segmentation so i wanted to ask here. Thanks in advance