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Erschienen in: Neural Computing and Applications 7/2024

06.12.2023 | Original Article

A deep learning approach to satellite image time series coregistration through alignment of road networks

verfasst von: Andres F. Pérez, Pooneh Maghoul, Ahmed Ashraf

Erschienen in: Neural Computing and Applications | Ausgabe 7/2024

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Abstract

The adverse effects of thawing permafrost on transportation infrastructure in northern regions are exacerbated by climate change. To address this issue, remote sensing techniques can be employed to track deformations in these structures over time. This will allow us to identify regions that are most vulnerable to permafrost degradation, and implement climate adaptation strategies accordingly. The Sentinel-2 mission provides highly suitable data for multitemporal analysis due to its high temporal resolution and multispectral coverage. However, the geometrical misalignment of Sentinel-2 imagery presents a significant challenge for such analysis. In this study, we propose an automatic sub-pixel coregistration algorithm for satellite image time series, specifically focusing on estimating the deformation of linear infrastructure in northern Canada. Our approach involves utilizing a deep learning model to generate binary masks of roads, which are then used to match and align the images. We demonstrate the feasibility of achieving sub-pixel coregistration through road alignment on a small dataset of high-resolution Sentinel-2 images from the town of Gillam in northern Canada. This represents an initial step toward training a road deformation prediction model, which can ultimately contribute to improved infrastructure resilience and adaptation to changing climatic conditions.

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Fußnoten
1
Mean SSIM between pairs of images for all the time series.
 
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Metadaten
Titel
A deep learning approach to satellite image time series coregistration through alignment of road networks
verfasst von
Andres F. Pérez
Pooneh Maghoul
Ahmed Ashraf
Publikationsdatum
06.12.2023
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2024
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-023-09242-0

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