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Titel:

Multi-modal Supervised Change Detection Data

Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
05.08.2021
Verantwortlich:
Zhu, Xiaoxiang
Autorinnen / Autoren:
Ebel, Patrick; Saha, Sudipan, Zhu, Xiaoxiang
Institutionszugehörigkeit:
TUM
Herausgeber:
TUM
Identifikator:
doi:10.14459/2021mp1619966
Enddatum der Datenerzeugung:
04.07.2021
Fachgebiet:
BAU Bauingenieurwesen, Vermessungswesen; DAT Datenverarbeitung, Informatik; GEO Geowissenschaften
Quellen der Daten:
Abbildungen von Objekten / image of objects
Datentyp:
Bilder / images
Methode der Datenerhebung:
Semi-automatic download and image preparation using Google Earth Engine, Python and GDAL, semi-automatic processing and clean-up
Beschreibung:
The Multi-modal Change Detection Data Set [1] provides Sentinel-1 dual-pol SAR measurements to complement the hand-labeled ONERA change detection data set of optical satellite observations [2]. Our data set consists of bi-temporal and full-scene SAR images for 24 globally distributed regions of interest. The full-scene images are geo-referenced, time-aligned and co-registered to the optical ONERA data [2] and provided in the form of 16-bit GeoTiffs containing the following information:
- Sentinel-1 SAR: 2 channels corresponding to sigma nought backscatter values in dB scale for VV and VH polarization.
[1] Ebel, P., Saha, S. and Zhu, X. X. (2021) Fusing Multi-modal Data for Supervised Change Detection. ISPRS. XXIV ISPRS Congress 2021, 04 - 10 July 2021, Nice, France / Virtual.
[2] Daudt, R.C., Le Saux, B., Boulch, A. and Gousseau, Y., 2018, July. Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2018 (pp. 2115-2118). IEEE.
Links:
Article:

https://elib.dlr.de/142284/

Schlagworte:
Remote sensing, deep learning, data fusion, synthetic aperture radar imagery, optical imagery, change detection
Technische Hinweise:
View and download (660 MB total, 2 files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1619966):
rsync rsync://m1619966@dataserv.ub.tum.de/m1619966/
Sprache:
en
Rechte:
by, http://creativecommons.org/licenses/by/4.0
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