Research Papers

Using phenological metrics and the multiple classifier fusion method to map land cover types

[+] Author Affiliations
Jianhong Liu

Beijing Normal University, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing 100875, China

Beijing Normal University, College of Resources Science and Technology, Beijing 100875, China

Northwest University, College of Urban and Environmental Science, Xi’an 710127, China

Yaozhong Pan

Beijing Normal University, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing 100875, China

Beijing Normal University, College of Resources Science and Technology, Beijing 100875, China

Xiufang Zhu

Beijing Normal University, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing 100875, China

Beijing Normal University, College of Resources Science and Technology, Beijing 100875, China

Wenquan Zhu

Beijing Normal University, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing 100875, China

Beijing Normal University, College of Resources Science and Technology, Beijing 100875, China

J. Appl. Remote Sens. 8(1), 083691 (Jan 20, 2014). doi:10.1117/1.JRS.8.083691
History: Received January 22, 2013; Revised December 9, 2013; Accepted December 12, 2013
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Abstract.  Feature selection and multiple classifier fusion (MCF) are effective approaches to improve land cover classification accuracy. In this study, we combined phenological metrics and the MCF method to map land cover types in Jiangsu province of China during the second crop growing season using moderate resolution imaging spectroradiometer time-series data. Eight phenological metrics were developed and calculated, and a MCF scheme was proposed by combining a simple majority vote and the measurement of posterior probabilities. The four base classifiers (i.e., the maximum likelihood classifier, the Mahalanobis distance classifier, the support vector machine classifier, and the neural networks classifier) and the MCF method were used in classifications using two spectral indices from the original satellite data (direct classification) and the computed metric data (metrics-based classification). Accuracy assessments indicated that the overall accuracies and kappa coefficients of the metrics-based classifications were all higher than those of direct classifications. The average overall accuracy and kappa coefficient of metrics-based classifications were 8.36% and 0.1 higher than that of direct classifications, respectively. Similarly, the overall accuracy and kappa coefficient of MCF generally were close to or exceeded the highest accuracy among all the base classifiers. The highest overall accuracy and kappa coefficient was achieved by classification with the MCF method based on phenological metrics (m-MCF), which were 88% and 0.85, respectively. Our results suggested that combining phenological metrics and MCF in classification is a promising method for land cover mapping in regions where strong phenological signals can be detected.

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Citation

Jianhong Liu ; Yaozhong Pan ; Xiufang Zhu and Wenquan Zhu
"Using phenological metrics and the multiple classifier fusion method to map land cover types", J. Appl. Remote Sens. 8(1), 083691 (Jan 20, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083691


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