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Immersive Visual Information Mining for Exploring the Content of EO Archives

Babaee, Mohammadreza and Bahmanyar, Reza and Rigoll, Gerald and Datcu, Mihai (2013) Immersive Visual Information Mining for Exploring the Content of EO Archives. In: Big Data from Space - Abstract-Book, p. 30. ESA. Big Data from Space, 5.-6. June 2013, Frascati, Italy.

Full text not available from this repository.

Official URL: http://congrexprojects.com/docs/default-source/13c10_docs/13c10_event_report.pdf?sfvrsn=2

Abstract

The amount of collected earth observation data is increasing intensively in order of several Terabytes of data a day. Simultaneously, new trends for exploration and information retrieval are highly demanded. Because recent proposed methods to explore EO data are based on the Image Information Mining IIM approach in which image features extraction, data reduction and labelling are the main steps, developing a new process chain, mainly based on human interaction, might be a promising solution. More precisely, human interacts with features in order to have an active learning system. The focus of this article is based on Immersive Visual Information Mining in which features/images are visualized and modified in an interactive immersive 3-D virtual environment (namely, CAVE) in order to change the learning process and eventually improve its performance. As the first step, the contents of images are extracted and represented by feature descriptors. A library of specific descriptors for multispectral and SAR is used: it comprises spectral-SIFT, spectral-WLD, color-histogram, color-SIFT and color-WLD. Thus, the whole archive is represented in the n-dimensional space of the extracted features, each patch being a point. In optical EO images, color-histogram can be extracted by concatenating the local histograms of colors for the three, RGB, channels. To build the two latter feature descriptors (color-SIFT and color-WLD) the spectral descriptors are applied individually to each color channel, and then they are concatenated to generate the final feature vectors. Each particular feature descriptor represents a particular aspect of the images.

Item URL in elib:https://elib.dlr.de/88972/
Document Type:Conference or Workshop Item (Speech)
Title:Immersive Visual Information Mining for Exploring the Content of EO Archives
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Babaee, MohammadrezaTU MunichUNSPECIFIED
Bahmanyar, Rezareza.bahmanyar (at) dlr.deUNSPECIFIED
Rigoll, GeraldTU MunichUNSPECIFIED
Datcu, Mihaimihai.datcu (at) dlr.deUNSPECIFIED
Date:2013
Journal or Publication Title:Big Data from Space - Abstract-Book
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:p. 30
Editors:
EditorsEmail
UNSPECIFIEDESA
Publisher:ESA
Status:Published
Keywords:EO Archives
Event Title:Big Data from Space
Event Location:Frascati, Italy
Event Type:international Conference
Event Dates:5.-6. June 2013
Organizer:ESA
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:INVALID USER
Deposited On:20 May 2014 12:40
Last Modified:21 May 2014 12:54

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