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Visual Analytics for Semantic Queries of TerraSAR-X Image Content

Espinoza-Molina, Daniela and Alonso, Kevin and Datcu, Mihai (2015) Visual Analytics for Semantic Queries of TerraSAR-X Image Content. In: Proceedings of SPIE, pp. 1-10. SPIE Digital Library. SPIE Remote Sensing Conference, 21-25 Sep 2015, Toulouse, France.

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Official URL: http://spie.org/remote-sensing-europe.xml

Abstract

With the continuous image product acquisition of satellite missions, the size of the image archives is considerably increasing every day as well as the variety and complexity of their content, surpassing the end-user capacity to analyse and exploit them. Advances in the image retrieval field have contributed to the development of tools for interactive exploration and extraction of the images from huge archives using different parameters like metadata, key-words, and basic image descriptors. Even though we count on more powerful tools for automated image retrieval and data analysis, we still face the problem of understanding and analyzing the results. Thus, a systematic computational analysis of these results is required in order to provide to the end-user a summary of the archive content in comprehensible terms. In this context, visual analytics combines automated analysis with interactive visualizations analysis techniques for an effective understanding, reasoning and decision making on the basis of very large and complex datasets. Moreover, currently several researches are focused on associating the content of the images with semantic definitions for describing the data in a format to be easily understood by the end-user. In this paper, we present our approach for computing visual analytics and semantically querying the TerraSAR-X archive. Our approach is mainly composed of four steps: 1) the generation of a data model that explains the information contained in a TerraSAR-X product. The model is formed by primitive descriptors and metadata entries, 2) the storage of this model in a database system, 3) the semantic definition of the image content based on machine learning algorithms and relevance feedback, and 4) querying the image archive using semantic descriptors as query parameters and computing the statistical nalysis of the query results. The experimental results shows that with the help of visual analytics and semantic definitions we are able to explain the image content using semantic terms and the relations between them answering questions such as what is the percentage of urban area in a region? or what is the distribution of water bodies in a city?.

Item URL in elib:https://elib.dlr.de/98015/
Document Type:Conference or Workshop Item (Speech)
Title:Visual Analytics for Semantic Queries of TerraSAR-X Image Content
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Espinoza-Molina, Danieladaniela.espinozamolina (at) dlr.deUNSPECIFIED
Alonso, Kevinkevin.alonsogonzalez (at) dlr.deUNSPECIFIED
Datcu, Mihaimihai.datcu (at) dlr.deUNSPECIFIED
Date:23 September 2015
Journal or Publication Title:Proceedings of SPIE
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-10
Editors:
EditorsEmail
UNSPECIFIEDSPIE Org.
Publisher:SPIE Digital Library
Status:Published
Keywords:data mining, knowledge discovery, semantic annotations, visual analytics
Event Title:SPIE Remote Sensing Conference
Event Location:Toulouse, France
Event Type:international Conference
Event Dates:21-25 Sep 2015
Organizer:SPIE
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 > Photogrammetry and Image Analysis
Deposited By: Espinoza Molina, Daniela
Deposited On:12 Oct 2015 10:55
Last Modified:31 Jul 2019 19:54

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