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Accelerated Probabilistic Learning Concept for Mining Heterogeneous Earth Observation Images

Alonso, Kevin and Datcu, Mihai (2015) Accelerated Probabilistic Learning Concept for Mining Heterogeneous Earth Observation Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (7), pp. 3356-3371. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/JSTARS.2015.2435491 ISSN 1939-1404

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Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7122869

Abstract

We present an accelerated probabilistic learning concept and its prototype implementation for mining heterogeneous Earth observation images, e.g., multispectral images, synthetic aperture radar (SAR) images, image time series, or geographical information systems (GIS) maps. The system prototype combines, at pixel level, the unsupervised clustering results of different features, extracted from heterogeneous satellite images and geographical information resources, with user-defined semantic annotations in order to calculate the posterior probabilities that allow the final probabilistic searches. The system is able to learn different semantic labels based on a newly developed Bayesian networks algorithm and allows different probabilistic retrieval methods of all semantically related images with only a few user interactions. The new algorithm reduces the computational cost, overperforming existing conventional systems, under certain conditions, by several orders of magnitude. The achieved speed-up allows the introduction of new feature models improving the learning capabilities of knowledge-driven image information mining systems and opening them to Big Data environments

Item URL in elib:https://elib.dlr.de/98186/
Document Type:Article
Title:Accelerated Probabilistic Learning Concept for Mining Heterogeneous Earth Observation Images
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Alonso, Kevinkevin.alonsogonzalez (at) dlr.deUNSPECIFIED
Datcu, Mihaimihai.datcu (at) dlr.deUNSPECIFIED
Date:12 June 2015
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:8
DOI :10.1109/JSTARS.2015.2435491
Page Range:pp. 3356-3371
Editors:
EditorsEmail
Chanussot, Jocelynjocelyn.chanussot@gipsa-lab.grenoble-inp.fr
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:Published
Keywords:Active learning (AL), Bayesian networks, Big Data bag-of-words (BoW), data fusion, geographical information systems (GIS), image mining
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:01 Oct 2015 10:41
Last Modified:31 Jul 2019 19:55

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