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A Comparative Study of Sample Selection Strategies Based on Optimum Experimental Design for SAR Image Classification

Cui, Shiyong and Datcu, Mihai (2014) A Comparative Study of Sample Selection Strategies Based on Optimum Experimental Design for SAR Image Classification. In: Proceedings of ESA-EUSC-JRC 2014 - 9th Conference on Image Information Mining Conference: The Sentinels Era, pp. 1-4. EU. ESA-EUSC-JRC 2014, 05 - 07 March 2014, Bucharest, Romania. DOI: 10.2788/25852 ISBN 978-92-79-36160-9 ISSN 1831-9424

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Official URL: https://earth.esa.int/web/guest/events/all-events/-/article/esa-eusc-jrc-2014-image-information-mining-conference

Abstract

In this paper, we evaluate sample selection strategies based on optimum experimental design for SAR image classification. Traditionally, support vector machine active learning is widely used by selecting the samples close to the decision surface. Recently, new methods based on optimum experimental design have been developed. To gain a complete understanding of these selection strategies, a comparative study on three approaches, transductive experimental design, manifold adaptive experimental design and locally linear reconstruction, has been performed for SAR image classification using different features. Among the three approaches,we show that manifold adaptive experimental design performs best and stably in terms of both accuracy and computational complexity.

Item URL in elib:https://elib.dlr.de/93402/
Document Type:Conference or Workshop Item (Speech)
Title:A Comparative Study of Sample Selection Strategies Based on Optimum Experimental Design for SAR Image Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Cui, ShiyongRemote Sensing Technology Institute (IMF)UNSPECIFIED
Datcu, Mihaimihai.datcu (at) dlr.deUNSPECIFIED
Date:2014
Journal or Publication Title:Proceedings of ESA-EUSC-JRC 2014 - 9th Conference on Image Information Mining Conference: The Sentinels Era
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.2788/25852
Page Range:pp. 1-4
Editors:
EditorsEmail
Soille, PierreJoint Research Centre, European Commission, Italy
Marchetti, Pier GiorgioEuropean Space Agency, Italy
Iapaolo, MicheleEuropean Space Agency, Italy
Colaiacomo, LucioEuropean Union Satellite Centre, Spain
Datcu, MihaiMF-PBA
Publisher:EU
ISSN:1831-9424
ISBN:978-92-79-36160-9
Status:Published
Keywords:Synthetic aperture radar (SAR), SAR image classification, Optimum experimental design (OED), Active learning, Support vector machine (SVM).
Event Title:ESA-EUSC-JRC 2014
Event Location:Bucharest, Romania
Event Type:international Conference
Event Dates:05 - 07 March 2014
Organizer:ESA/JRC
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: Cui, Shiyong
Deposited On:11 Dec 2014 12:22
Last Modified:03 Feb 2015 12:59

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