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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:
- 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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