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Titel:

MDAS: A New Multimodal Benchmark Dataset for Remote Sensing

Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
30.09.2022
Verantwortlich:
Zhu, Xiao Xiang
Autorinnen / Autoren:
Hu, Jingliang; Liu, Rong; Hong, Danfeng; Camero, Andrés; Yao, Jing; Schneider, Mathias; Kurz, Franz; Segl, Karl; Zhu, Xiao Xiang
Institutionszugehörigkeit:
Technichal University of Munich (TUM): Hu, Jingliang; Liu, Rong; Zhu, Xiao Xiang
German Aerospace Center (DLR): Hong, Danfeng; Camero, Andrés; Schneider, Mathias; Kurz, Franz
Chinese Academy of Sciences: Yao,Jing
German Research Center for Geosciences (GFZ): Segl, Karl
Herausgeber:
TUM
Identifikator:
doi:10.14459/2022mp1657312
Enddatum der Datenerzeugung:
24.08.2021
Fachgebiet:
BAU Bauingenieurwesen, Vermessungswesen; DAT Datenverarbeitung, Informatik; GEO Geowissenschaften
zusätzliche Fachgebiete:
Remote Sensing
Quellen der Daten:
Abbildungen von Objekten / image of objects
Datentyp:
Bilder / images
Methode der Datenerhebung:
Processing of Sentinel-1 imagery using ESA SNAP toolbox. Sentinel-2 L2A product fetched from Sentinel Hub. HySpex (hyperspectral) data acquired by the Remote Sensing Technology Institute of DLR in 23 flight strips (on a Dornier DO228-212 plane). Digital Surface Model (DSM) acquired with 3K camera in 23 flight strips. Geographic Information System (GIS) data downloaded from Open Street Map. Manually labeled data.
Beschreibung:
In Earth observation, multimodal data fusion is an intuitive strategy to break the limitation of individual data. Complementary physical contents of data sources allow comprehensive and precise information retrieve. Future applications will have many options on data sources. Such privilege can be beneficial only if algorithms are ready to work with various data sources. However, current data fusion studies mostly focus on the fusion of two data sources. Thus, we provide the community a benc...     »
Schlagworte:
multimodal data fusion; Sentinel-1; Sentinel-2; DLR 3K DSM; HySpex; EnMAP; Synthetic aperture radar; Multi/hyper-spectral image; benchmark data set; super resolution; spectral unmixing; land cover classification
Technische Hinweise:
View and download (35GB total, 1 File)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1657312):
rsync rsync://m1657312@dataserv.ub.tum.de/m1657312/
Sprache:
en
Rechte:
by-sa, http://creativecommons.org/licenses/by-sa/4.0
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