Header

UZH-Logo

Maintenance Infos

Geospatial correspondences for multimodal registration


Marcos-Gonzalez, Diego; Hamid, Raffay; Tuia, Devis (2016). Geospatial correspondences for multimodal registration. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 26 June 2016 - 1 July 2016, 5091-5100.

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 naϊve 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.

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 naϊve 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.

Statistics

Altmetrics

Downloads

1 download since deposited on 24 Jan 2017
1 download since 12 months
Detailed statistics

Additional indexing

Item Type:Conference or Workshop Item (Paper), not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:1 July 2016
Deposited On:24 Jan 2017 16:13
Last Modified:31 Mar 2017 07:04
Publisher:IEEE Xplore
ISBN:978-1-4673-8851-1
Additional Information:Proceedings 29th IEEE Conference on Computer Vision and Pattern Recognition
Publisher DOI:https://doi.org/10.1109/CVPR.2016.550
Related URLs:http://ieeexplore.ieee.org/document/7780919/

Download

Preview Icon on Download
Content: Published Version
Language: English
Filetype: PDF - Registered users only
Size: 4MB
View at publisher

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations