Images acquired by Synthetic Aperture Radar (SAR) are affected by speckle, making their interpretation difficult. Most recently, the rise of deep learning algorithms has led to groundbreaking results. The training of a neural network typically requires matched pairs of speckled / speckle-free images. To account for the speckle present in actual images and simplify the generation of training sets, self-supervision approaches directly train the network on speckled SAR data. Self-supervision exploits a form of diversity, either temporal, spatial, or based on the real/imaginary parts. We compare the requirements in terms of data preprocessing and the performance of three self-supervised strategies.