Image registration of satellite imagery with deep convolutional neural networks

Maria Vakalopoulou, Stergios Christodoulidis, Mihir Sahasrabudhe, Stavroula Mougiakakou, and Nikos Paragios.

IGARSS 2019

Abstract: Image registration in multimodal, multitemporal satellite imagery is one of the most important problems in remote sensing and essential for a number of other tasks such as change detection and image fusion. In this paper, inspired by the recent success of deep learning approaches we propose a novel convolutional neural network architecture that couples linear and deformable approaches for accurate alignment of remote sensing imagery. The proposed method is completely unsupervised, ensures smooth displacement fields and provides real time registration on a pair of images. We evaluate the performance of our method using a challenging multitemporal dataset of very high resolution satellite images and compare its performance with a state of the art elastic registration method based on graphical models. Both quantitative and qualitative results prove the high potentials of our method.

@inproceedings{vakalopoulou2019image,
  title={Image registration of satellite imagery with deep convolutional neural networks},
  author={Vakalopoulou, Maria and Christodoulidis, Stergios and Sahasrabudhe, Mihir and Mougiakakou, Stavroula and Paragios, Nikos},
  booktitle={IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium},
  pages={4939--4942},
  year={2019},
  organization={IEEE}
}