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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.
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https://hal.inria.fr/hal-02422555
Contributor : Maria Vakalopoulou <>
Submitted on : Sunday, December 22, 2019 - 4:18:27 PM
Last modification on : Thursday, July 9, 2020 - 4:06:04 PM
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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⟩

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