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  Active learning for classification of remote sensing images

Bruzzone, L., & Persello, C. (2009). Active learning for classification of remote sensing images. In 2009 IEEE International Geoscience and Remote Sensing Symposium (pp. III-693-III-696). Piscataway, NJ, USA: IEEE.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C3FB-C Versions-Permalink: https://hdl.handle.net/21.11116/0000-0002-EAD0-B
Genre: Konferenzbeitrag

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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5417857 (Verlagsversion)
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 Urheber:
Bruzzone, L, Autor
Persello, C1, Autor           
Affiliations:
1External Organizations, ou_persistent22              

Inhalt

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Schlagwörter: -
 Zusammenfassung: This paper presents an analysis of active learning techniques for the classification of remote sensing images and proposes a novel active learning method based on support vector machines (SVMs). The proposed method exploits a query function for the inclusion of batches of unlabeled samples in the training set, which is based on the evaluation of two criteria: uncertainty and diversity. This query function adopts a stochastic approach to the selection of unlabeled samples, which is based on a function of uncertainty estimated from the distribution of errors on the validation set (which is assumed available for the model selection of the SVM classifier). Experimental results carried out on a very high resolution image confirm the effectiveness of the proposed active learning technique, which results more accurate than standard methods.

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 Datum: 2009-07
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1109/IGARSS.2009.5417857
 Art des Abschluß: -

Veranstaltung

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Titel: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009)
Veranstaltungsort: Cape Town, South Africa
Start-/Enddatum: 2009-07-12 - 2009-07-17

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Titel: 2009 IEEE International Geoscience and Remote Sensing Symposium
Genre der Quelle: Konferenzband
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Piscataway, NJ, USA : IEEE
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: III-693 - III-696 Identifikator: ISBN: 978-1-4244-3395-7