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Open AccessArticle

OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification

1
Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
2
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
3
College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
4
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 987; https://doi.org/10.3390/rs12060987
Received: 18 February 2020 / Revised: 11 March 2020 / Accepted: 16 March 2020 / Published: 19 March 2020
Land cover samples are usually the foundation for supervised classification. Unfortunately, for land cover mapping in large areas, only limited samples can be used due to the time-consuming and labor-intensive sample collection. A novel and practical Object-oriented Iterative Classification method based on Multiple Classifiers Ensemble (OIC-MCE) was proposed in this paper. It systematically integrated object-oriented segmentation, Multiple Classifier Ensemble (MCE), and Iterative Classification (IC). In this method, the initial training samples were updated self-adaptively during the iterative processes. Based on these updated training samples, the inconsistent regions (ICR) in the classification results of the MCE method were reclassified to reduce their uncertainty. Three typical case studies in the China-Pakistan Economic Corridor (CPEC) indicate that the overall accuracy of the OIC-MCE method is significantly higher than that of the single classifier. After five iterations, the overall accuracy of the OIC-MCE approach increased by 5.58%–8.38% compared to the accuracy of the traditional MCE method. The spatial distribution of newly added training samples generated by the OIC-MCE approach was relatively uniform. It was confirmed by ten repeated experiments that the OIC-MCE approach has good stability. More importantly, even if the initial sample size reduced by 65%, the quality of the final classification result based on the proposed OIC-MCE approach would not be greatly affected. Therefore, the proposed OIC-MCE approach provides a new solution for land cover mapping with limited samples. Certainly, it is also well suited for land cover mapping with abundant samples. View Full-Text
Keywords: land cover mapping; multiple classifier ensemble (MCE); iterative classification (IC); self-adaptive updating of samples; China-Pakistan economic corridor (CPEC); remote sensing land cover mapping; multiple classifier ensemble (MCE); iterative classification (IC); self-adaptive updating of samples; China-Pakistan economic corridor (CPEC); remote sensing
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MDPI and ACS Style

Lei, G.; Li, A.; Bian, J.; Yan, H.; Zhang, L.; Zhang, Z.; Nan, X. OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification. Remote Sens. 2020, 12, 987.

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