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Remote Sens. 2017, 9(10), 1055; https://doi.org/10.3390/rs9101055

Improving Land Use/Cover Classification with a Multiple Classifier System Using AdaBoost Integration Technique

1
School of Geography and Planning, Sun Yat-sen University, No. 135 Xinggangxi Road, Guangzhou 510275, China
2
Department of Geography, Florida State University, Tallahassee, FL 32306-2190, USA
*
Author to whom correspondence should be addressed.
Received: 19 August 2017 / Revised: 21 September 2017 / Accepted: 10 October 2017 / Published: 17 October 2017
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Abstract

Guangzhou has experienced a rapid urbanization since 1978 when China initiated the economic reform, resulting in significant land use/cover changes (LUC). To produce a time series of accurate LUC dataset that can be used to study urbanization and its impacts, Landsat imagery was used to map LUC changes in Guangzhou from 1987 to 2015 at a three-year interval using a multiple classifier system (MCS). The system was based on a weighted vector to combine base classifiers of different classification algorithms, and was improved using the AdaBoost technique. The new classification method used support vector machines (SVM), C4.5 decision tree, and neural networks (ANN) as the training algorithms of the base classifiers, and produced higher overall classification accuracy (88.12%) and Kappa coefficient (0.87) than each base classifier did. The results of the experiment showed that, based on the accuracy improvement of each class, the overall accuracy was improved effectively, which combined advantages from each base classifier. The new method is of high robustness and low risk of overfitting, and is reliable and accurate, and could be used for analyzing urbanization processes and its impacts. View Full-Text
Keywords: multiple classifiers system (MCS); AdaBoost; remote sensing; classification algorithms multiple classifiers system (MCS); AdaBoost; remote sensing; classification algorithms
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Chen, Y.; Dou, P.; Yang, X. Improving Land Use/Cover Classification with a Multiple Classifier System Using AdaBoost Integration Technique. Remote Sens. 2017, 9, 1055.

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