Land cover classification using random forest with genetic algorithm-based parameter optimization

Dongping Ming; Tianning Zhou; Min Wang; Tian Tan

doi:10.1117/1.JRS.10.035021

12 September 2016 Land cover classification using random forest with genetic algorithm-based parameter optimization

Dongping Ming, Tianning Zhou, Min Wang, Tian Tan

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 3, 035021 (September 2016). https://doi.org/10.1117/1.JRS.10.035021

Abstract

Land cover classification based on remote sensing imagery is an important means to monitor, evaluate, and manage land resources. However, it requires robust classification methods that allow accurate mapping of complex land cover categories. Random forest (RF) is a powerful machine-learning classifier that can be used in land remote sensing. However, two important parameters of RF classification, namely, the number of trees and the number of variables tried at each split, affect classification accuracy. Thus, optimal parameter selection is an inevitable problem in RF-based image classification. This study uses the genetic algorithm (GA) to optimize the two parameters of RF to produce optimal land cover classification accuracy. HJ-1B CCD2 image data are used to classify six different land cover categories in Changping, Beijing, China. Experimental results show that GA-RF can avoid arbitrariness in the selection of parameters. The experiments also compare land cover classification results by using GA-RF method, traditional RF method (with default parameters), and support vector machine method. When the GA-RF method is used, classification accuracies, respectively, improved by 1.02% and 6.64%. The comparison results show that GA-RF is a feasible solution for land cover classification without compromising accuracy or incurring excessive time.

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Dongping Ming, Tianning Zhou, Min Wang, and Tian Tan "Land cover classification using random forest with genetic algorithm-based parameter optimization," Journal of Applied Remote Sensing 10(3), 035021 (12 September 2016). https://doi.org/10.1117/1.JRS.10.035021

Published: 12 September 2016

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