Image and Signal Processing Methods

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

[+] Author Affiliations
Dongping Ming, Tian Tan

China University of Geosciences (Beijing), School of Information Engineering, 29 Xueyuan Road, Haidian, Beijing 100083, China

Tianning Zhou

BGP, CNPC, Measuring Service Center, Equipment Service Department, Fanyang Middle Road 309, Zhuozhou, Hebei 072750, China

Min Wang

Nanjing Normal University, Key Laboratory of Virtual Geographic Environment, Ministry of Education, 1 Wenyuan Road, Xianlin, Nanjing, Jiangsu 210023, China

Jiangsu Center for Collaborative Innovation, Geographical Information Resource Development and Application, 1 Wenyuan Road, Xianlin, Nanjing, Jiangsu 210023, China

J. Appl. Remote Sens. 10(3), 035021 (Sep 12, 2016). doi:10.1117/1.JRS.10.035021
History: Received April 19, 2016; Accepted August 23, 2016
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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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© 2016 Society of Photo-Optical Instrumentation Engineers

Topics

Land cover

Citation

Dongping Ming ; Tianning Zhou ; Min Wang and Tian Tan
"Land cover classification using random forest with genetic algorithm-based parameter optimization", J. Appl. Remote Sens. 10(3), 035021 (Sep 12, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.035021


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