Deep Learning based Multistep Registration Focusing on Regions of Change

During the last decades, the remote sensing community has gained access to a wide satellite imagery material, resulting in great progress on various applications. Most of these applications firstly require that the employed images are in the same coordinate system, without any registration errors that will deteriorate their performance. In this work we employ a multistep fully convolutional network in order to improve the image registration task. Moreover, we propose the relaxation of the registration constraints on the regions where changes have occurred. In this way, the model learns to keep the proper structures of objects that change in the multitemporal pair while at the same time provides dense deformations for the rest of the region. Our method is very efficient, fast and requires annotations for the regions of change only in training time. We conduct experiments on the very high resolution Attica VHR dataset comparing it with other deformable registration approaches from the literature. Our method outperforms all compared methods both quantitatively and qualitatively, setting up a foundation for further future research.