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An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA

An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA
An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA
The iterative and convergent nature of ensemble learning algorithms provides potential for improving classification of complex landscapes. This study performs land-cover classification in a heterogeneous Massachusetts landscape by comparing three ensemble learning techniques (bagging, boosting, and random forests) and a non-ensemble learning algorithm (classification trees) using multiple criteria related to algorithm and training data characteristics. The ensemble learning algorithms had comparably high accuracy (Kappa range: 0.76-0.78), which was 11% higher than that of classification trees. Ensemble learning techniques were not influenced by calibration data variability, were robust to one-fifth calibration data noise, and insensitive to a 50% reduction in calibration data size.
1548-1603
623-643
Ghimire, Bardan
cfcb397b-39ac-41d2-adef-2469809e547f
Rogan, John
acb0fe8d-4229-4d48-845d-d86c07c65226
Rodríguez Galiano, Víctor
6c733a16-e047-4844-8b04-e2e64c4721cb
Panday, Prajjwal
116c0330-8a8d-4ea3-88e6-ca61908615c7
Neeti, Neeti
04382933-631e-481f-972f-a45278884133
Ghimire, Bardan
cfcb397b-39ac-41d2-adef-2469809e547f
Rogan, John
acb0fe8d-4229-4d48-845d-d86c07c65226
Rodríguez Galiano, Víctor
6c733a16-e047-4844-8b04-e2e64c4721cb
Panday, Prajjwal
116c0330-8a8d-4ea3-88e6-ca61908615c7
Neeti, Neeti
04382933-631e-481f-972f-a45278884133

Ghimire, Bardan, Rogan, John, Rodríguez Galiano, Víctor, Panday, Prajjwal and Neeti, Neeti (2012) An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA. GIScience & Remote Sensing, 49 (5), 623-643. (doi:10.2747/1548-1603.49.5.623).

Record type: Article

Abstract

The iterative and convergent nature of ensemble learning algorithms provides potential for improving classification of complex landscapes. This study performs land-cover classification in a heterogeneous Massachusetts landscape by comparing three ensemble learning techniques (bagging, boosting, and random forests) and a non-ensemble learning algorithm (classification trees) using multiple criteria related to algorithm and training data characteristics. The ensemble learning algorithms had comparably high accuracy (Kappa range: 0.76-0.78), which was 11% higher than that of classification trees. Ensemble learning techniques were not influenced by calibration data variability, were robust to one-fifth calibration data noise, and insensitive to a 50% reduction in calibration data size.

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More information

Published date: September 2012
Organisations: Global Env Change & Earth Observation

Identifiers

Local EPrints ID: 360081
URI: http://eprints.soton.ac.uk/id/eprint/360081
ISSN: 1548-1603
PURE UUID: 44373e47-03b5-4680-9287-2e1966fcf141

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Date deposited: 25 Nov 2013 13:29
Last modified: 14 Mar 2024 15:33

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Contributors

Author: Bardan Ghimire
Author: John Rogan
Author: Víctor Rodríguez Galiano
Author: Prajjwal Panday
Author: Neeti Neeti

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