Boosting, bagging, and consensus based classification of multisource remote sensing data

Gunnar Jakob Briem; Jon Atli Benediktsson; Johannes R. Sveinsson

doi:10.1007/3-540-48219-9_28

Boosting, bagging, and consensus based classification of multisource remote sensing data

Gunnar Jakob Briem, Jon Atli Benediktsson, Johannes R. Sveinsson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Citations (Scopus)

Abstract

The need to optimize the classification accuracy of remotely sensed imagery has led to an increasing use of Earth observation data with different characteristics collected from a variety of sensors from different parts of the electromagnetic spectrum. Combining multisource data is believed to offer enhanced capabilities for the classification of target surfaces. In the paper several single and multiple classifiers which are appropriate for classification of multisource remote sensing and geographic data are considered. The focus is on multiple classifiers: bagging algorithms, boosting algorithms, and consensus theoretic classifiers. These multiple classifiers have different characteristics. The performance of the algorithms in terms of accuracies is compared for a multisource remote sensing and geographic data set.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Josef Kittler, Fabio Roli
Publisher	Springer Verlag
Pages	279-288
Number of pages	10
ISBN (Print)	3540422846, 9783540422846
DOIs	https://doi.org/10.1007/3-540-48219-9_28
Publication status	Published - 2001
Event	2nd International Workshop on Multiple Classifier Systems, MCS 2001 - Cambridge, United Kingdom Duration: 2 Jul 2001 → 4 Jul 2001

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2096
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Workshop on Multiple Classifier Systems, MCS 2001
Country/Territory	United Kingdom
City	Cambridge
Period	2/07/01 → 4/07/01

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.

Access to Document

10.1007/3-540-48219-9_28

Cite this

Briem, G. J., Benediktsson, J. A., & Sveinsson, J. R. (2001). Boosting, bagging, and consensus based classification of multisource remote sensing data. In J. Kittler, & F. Roli (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 279-288). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2096). Springer Verlag. https://doi.org/10.1007/3-540-48219-9_28

Briem, Gunnar Jakob ; Benediktsson, Jon Atli ; Sveinsson, Johannes R. / Boosting, bagging, and consensus based classification of multisource remote sensing data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Josef Kittler ; Fabio Roli. Springer Verlag, 2001. pp. 279-288 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The need to optimize the classification accuracy of remotely sensed imagery has led to an increasing use of Earth observation data with different characteristics collected from a variety of sensors from different parts of the electromagnetic spectrum. Combining multisource data is believed to offer enhanced capabilities for the classification of target surfaces. In the paper several single and multiple classifiers which are appropriate for classification of multisource remote sensing and geographic data are considered. The focus is on multiple classifiers: bagging algorithms, boosting algorithms, and consensus theoretic classifiers. These multiple classifiers have different characteristics. The performance of the algorithms in terms of accuracies is compared for a multisource remote sensing and geographic data set.",

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Briem, GJ, Benediktsson, JA & Sveinsson, JR 2001, Boosting, bagging, and consensus based classification of multisource remote sensing data. in J Kittler & F Roli (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2096, Springer Verlag, pp. 279-288, 2nd International Workshop on Multiple Classifier Systems, MCS 2001, Cambridge, United Kingdom, 2/07/01. https://doi.org/10.1007/3-540-48219-9_28

Boosting, bagging, and consensus based classification of multisource remote sensing data. / Briem, Gunnar Jakob; Benediktsson, Jon Atli; Sveinsson, Johannes R.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Josef Kittler; Fabio Roli. Springer Verlag, 2001. p. 279-288 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2096).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Briem GJ, Benediktsson JA, Sveinsson JR. Boosting, bagging, and consensus based classification of multisource remote sensing data. In Kittler J, Roli F, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2001. p. 279-288. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-48219-9_28

Boosting, bagging, and consensus based classification of multisource remote sensing data

Abstract

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Cite this