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Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery

State Key Laboratory of Urban and Region Ecology, Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences, Shuangqinglu 18, Beijing 100085, China
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Academic Editors: Ioannis Gitas and Prasad S. Thenkabail
Remote Sens. 2015, 7(1), 153-168; https://doi.org/10.3390/rs70100153
Received: 13 October 2014 / Accepted: 15 December 2014 / Published: 24 December 2014
This study evaluates and compares the performance of four machine learning classifiers—support vector machine (SVM), normal Bayes (NB), classification and regression tree (CART) and K nearest neighbor (KNN)—to classify very high resolution images, using an object-based classification procedure. In particular, we investigated how tuning parameters affect the classification accuracy with different training sample sizes. We found that: (1) SVM and NB were superior to CART and KNN, and both could achieve high classification accuracy (>90%); (2) the setting of tuning parameters greatly affected classification accuracy, particularly for the most commonly-used SVM classifier; the optimal values of tuning parameters might vary slightly with the size of training samples; (3) the size of training sample also greatly affected the classification accuracy, when the size of training sample was less than 125. Increasing the size of training samples generally led to the increase of classification accuracies for all four classifiers. In addition, NB and KNN were more sensitive to the sample sizes. This research provides insights into the selection of classifiers and the size of training samples. It also highlights the importance of the appropriate setting of tuning parameters for different machine learning classifiers and provides useful information for optimizing these parameters. View Full-Text
Keywords: object-based classification; machine learning classifiers; very high resolution image; urban area; tuning parameters object-based classification; machine learning classifiers; very high resolution image; urban area; tuning parameters
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MDPI and ACS Style

Qian, Y.; Zhou, W.; Yan, J.; Li, W.; Han, L. Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery. Remote Sens. 2015, 7, 153-168. https://doi.org/10.3390/rs70100153

AMA Style

Qian Y, Zhou W, Yan J, Li W, Han L. Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery. Remote Sensing. 2015; 7(1):153-168. https://doi.org/10.3390/rs70100153

Chicago/Turabian Style

Qian, Yuguo, Weiqi Zhou, Jingli Yan, Weifeng Li, and Lijian Han. 2015. "Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery" Remote Sensing 7, no. 1: 153-168. https://doi.org/10.3390/rs70100153

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