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Study Cases on Fast Compression Distance Based Data Visualization

Yao, Wei (2019) Study Cases on Fast Compression Distance Based Data Visualization. IGARSS 2019, 28 Jul-02 Aug 2019, Yokohama, Japan.

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Official URL: https://igarss2019.org/Papers/ViewPapers_MS.asp?PaperNum=3775


In this paper, we develop a visualization tool to enhance the understanding of up to big data sets. Compared to classic data models which rely on the computing of the features (color, texture, etc.), this tool is fully feature free, as it processes directly on the data file. The Fast Compression Distance (FCD) and t-distributed Stochastic Neighbor Embedding (t-SNE) have been applied to visualize a large TerraSAR-X dataset which are annotated with up to three layers of hierarchical semantic labels, and a Sentinel-1 dataset with 10 annotated classes, in VV and VH polarization modes. We analyze the visualization results in manifold space, and try to understand and interpret them with the available semantic labels. The visualization interpretation is based on a vega-style interactive tool, which allows user zoom in, zoom out for processing large amount of data points.

Item URL in elib:https://elib.dlr.de/131118/
Document Type:Conference or Workshop Item (Speech)
Title:Study Cases on Fast Compression Distance Based Data Visualization
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Yao, WeiWei.Yao (at) dlr.deUNSPECIFIED
Date:August 2019
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1-4
Keywords:FCD, t-SNE, TerraSAR-X, Sentinel-1, visualization
Event Title:IGARSS 2019
Event Location:Yokohama, Japan
Event Type:international Conference
Event Dates:28 Jul-02 Aug 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Karmakar, Chandrabali
Deposited On:04 Dec 2019 15:01
Last Modified:04 Dec 2019 15:01

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