Combining random forest and support vector machines for object-based rural-land-cover classification using high spatial resolution imagery

Saiping Xu; Qianjun Zhao; Kai Yin; Feifei Zhang; Dingbang Liu; Guang Yang

doi:10.1117/1.JRS.13.014521

23 February 2019 Combining random forest and support vector machines for object-based rural-land-cover classification using high spatial resolution imagery

Saiping Xu, Qianjun Zhao, Kai Yin, Feifei Zhang, Dingbang Liu, Guang Yang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 13, Issue 1, 014521 (February 2019). https://doi.org/10.1117/1.JRS.13.014521

Abstract

Land-cover classification using remote sensing imagery is an important part of environmental research because it provides baseline information for ecological vulnerability and risk assessment, disaster management, landscape conservation, local and regional planning, and so on. Rural-land-cover classification is challenging for both object-based image analysis methods and classifiers. The objective of this study is to improve the object-oriented classification accuracy of rural land cover by combining two models based on high spatial resolution imagery. We apply the C5.0 algorithm in R to combine support vector machines (SVMs) and random forest (RF) to create the model RS_C5.0. The effectiveness of the model combination is assessed by comparing the classification results with the state-of-the-art machine learning algorithm, namely extreme gradient boosting (XGBoost). The comparisons are done based on the classification results of both the study area and the case area. Results show that in the classification of the study area, RF performs slightly better than SVM, and XGBoost performs worse than RF but better than SVM. However, in the classification of the case area, SVM performs slightly better than RF and both SVM and RF perform better than XGBoost. Furthermore, RS_C5.0 obtains the highest overall accuracies and kappa coefficients in the classifications of both the study area and the case area. In terms of training time, XGBoost runs the slowest in the classifications of both the study area and the case area. SVM and RF as well as the combined model (RS_C5.0) run much faster than XGBoost classifier. To summarize, the combination of SVM and RF classifiers using C5.0 algorithm is found to be a fast and effective way to improve rural-land-cover classification.

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Saiping Xu, Qianjun Zhao, Kai Yin, Feifei Zhang, Dingbang Liu, and Guang Yang "Combining random forest and support vector machines for object-based rural-land-cover classification using high spatial resolution imagery," Journal of Applied Remote Sensing 13(1), 014521 (23 February 2019). https://doi.org/10.1117/1.JRS.13.014521

Received: 3 September 2018; Accepted: 1 February 2019; Published: 23 February 2019

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