Deep learning methods are widely used in the domain of change detection in remote sensing images. While datasets of that kind are abundant, annotated images, specific for the task at hand, are still scarce. Neural networks trained with Self supervised learning aim to harness large volumes of unlabeled satellite high resolution images to help in finding better solutions for the change detection problem. In this paper we experiment with this approach by presenting 4 different change detection methodologies. We propose a fusion method that under specific parameters can provide better results. We evaluate our results using two openly available datasets with Sentinel-2 satellite images, S2MTCP and OSCD, and we investigate the impact of using 2 different Sentinel 2 band combinations on our final predictions. Finally we conclude by summarizing the benefits of this approach as well as we propose future areas of interest that could be of value in enhancing the change detection task’s outcomes.