Open access
Datum
2022-11Typ
- Conference Paper
Abstract
Image registration that aligns multi-temporal or multi-source images
is vital for tasks like change detection and image fusion.
Thanks to the advance and large-scale practice of modern surveying
methods, multi-temporal historical maps can be unlocked and
combined to trace object changes in the past, potentially supporting
research in environmental science, ecology and urban planning,
etc. Even when maps are geo-referenced, the contained geographical
features can be misaligned due to surveying, painting, map
generalization, and production bias. In our work, we adapt an endto-
end unsupervised deformation network that couples rigid and
non-rigid transformations to align scanned historical map sheets
at different time stamps. To the best of our knowledge, we are the
first to use unsupervised deep learning to register map images. We
address the sparsity of map features by introducing a loss based on
distance fields. When aligning the displaced landmark locations by
our proposed method, the results are promising both quantitatively
and qualitatively. The generated smooth deformation grid can be
applied to vector features directly to align them from the source
map sheet to the target map sheet. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000581333Publikationsstatus
publishedExterne Links
Buchtitel
GeoAI '22: Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge DiscoverySeiten / Artikelnummer
Verlag
Association for Computing MachineryKonferenz
Thema
GIS; image registration; historical maps; deep learning; unsupervised neural networksOrganisationseinheit
03466 - Hurni, Lorenz / Hurni, Lorenz
Förderung
188692 - HistoRiCH: Historical river change – Planning for the future by exploring the mapped past (SNF)