Skip to Main content Skip to Navigation
New interface
Conference papers

Image Registration of Satellite Imagery with Deep Convolutional Neural Networks

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 unsu-pervised, ensures smooth displacement fields and provides real time registration on a pair of images. We evaluate the performance of our method using a challenging multitempo-ral 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.
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download
Contributor : Maria Vakalopoulou Connect in order to contact the contributor
Submitted on : Sunday, December 22, 2019 - 4:18:27 PM
Last modification on : Thursday, February 3, 2022 - 3:09:48 AM
Long-term archiving on: : Monday, March 23, 2020 - 2:45:08 PM


Files produced by the author(s)



Maria Vakalopoulou, Stergios Christodoulidis, Mihir Sahasrabudhe, Stavroula Mougiakakou, Nikos Paragios. Image Registration of Satellite Imagery with Deep Convolutional Neural Networks. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, France. pp.4939-4942, ⟨10.1109/IGARSS.2019.8898220⟩. ⟨hal-02422555⟩



Record views


Files downloads