Geospatial correspondences for multimodal registration

Diego Marcos, Raffay Hamid, Devis Tuia

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

18 Citations (Scopus)

Abstract

The growing availability of very high resolution (<1 m/pixel) satellite and aerial images has opened up unprecedented opportunities to monitor and analyze the evolution of land-cover and land-use across the world. To do so, images of the same geographical areas acquired at different times and, potentially, with different sensors must be efficiently parsed to update maps and detect land-cover changes. However, a naive transfer of ground truth labels from one location in the source image to the corresponding location in the target image is generally not feasible, as these images are often only loosely registered (with up to ± 50m of non-uniform errors). Furthermore, land-cover changes in an area over time must be taken into account for an accurate ground truth transfer. To tackle these challenges, we propose a mid-level sensor-invariant representation that encodes image regions in terms of the spatial distribution of their spectral neighbors. We incorporate this representation in a Markov Random Field to simultaneously account for nonlinear mis-registrations and enforce locality priors to find matches between multi-sensor images. We show how our approach can be used to assist in several multimodal land-cover update and change detection problems.

Original languageEnglish
Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
Place of PublicationLas Vegas
PublisherIEEE computer society
Pages5091-5100
Number of pages10
ISBN (Electronic)9781467388504
ISBN (Print)9781467388511
DOIs
Publication statusPublished - 9 Dec 2016
Externally publishedYes
Event29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States
Duration: 26 Jun 20161 Jul 2016

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2016-December
ISSN (Print)1063-6919

Conference

Conference29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
CountryUnited States
CityLas Vegas
Period26/06/161/07/16

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  • Cite this

    Marcos, D., Hamid, R., & Tuia, D. (2016). Geospatial correspondences for multimodal registration. In Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 5091-5100). [7780919] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2016-December). IEEE computer society. https://doi.org/10.1109/CVPR.2016.550