Image Registration of Satellite Imagery with Deep Convolutional Neural Networks

Vakalopoulou, Maria; Christodoulidis, Stergios; Sahasrabudhe, Mihir; Mougiakakou, Stavroula; Paragios, Nikos (14 November 2019). Image Registration of Satellite Imagery with Deep Convolutional Neural Networks. In: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 4939-4942). IEEE 10.1109/IGARSS.2019.8898220

Text 08898220(1).pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (17MB) | Request a copy

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.

Interest & Impact

Downloads

2 since deposited on 19 Dec 2019
0 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item