Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping-A Case Study of the Loess Plateau, China

	Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping-A Case Study of the Loess Plateau, China
	Ding, Hu; Na, Jiaming; Jiang, Shangjing; Zhu, Jie; Liu, Kai; Fu, Yingchun; Li, Fayuan
刊名	REMOTE SENSING
	2021
卷号	13 期号:5
英文摘要	Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. As a result of the importance of the contextual information for terrace mapping, object-based image analysis (OBIA) combined with machine learning (ML) technologies are widely used. However, the selection of an appropriate classifier is of great importance for the terrace mapping task. In this study, the performance of an integrated framework using OBIA and ML for terrace mapping was tested. A catchment, Zhifanggou, in the Loess Plateau, China, was used as the study area. First, optimized image segmentation was conducted. Then, features from the DEMs and imagery were extracted, and the correlations between the features were analyzed and ranked for classification. Finally, three different commonly-used ML classifiers, namely, extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN), were used for terrace mapping. The comparison with the ground truth, as delineated by field survey, indicated that random forest performed best, with a 95.60% overall accuracy (followed by 94.16% and 92.33% for XGBoost and KNN, respectively). The influence of class imbalance and feature selection is discussed. This work provides a credible framework for mapping artificial terraces.
内容类型	期刊论文
源URL	[http://159.226.73.51/handle/332005/20607]
专题	中国科学院南京地理与湖泊研究所
推荐引用方式 GB/T 7714	Ding, Hu,Na, Jiaming,Jiang, Shangjing,et al. Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping-A Case Study of the Loess Plateau, China[J]. REMOTE SENSING,2021,13(5).
APA	Ding, Hu.,Na, Jiaming.,Jiang, Shangjing.,Zhu, Jie.,Liu, Kai.,...&Li, Fayuan.(2021).Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping-A Case Study of the Loess Plateau, China.REMOTE SENSING,13(5).
MLA	Ding, Hu,et al."Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping-A Case Study of the Loess Plateau, China".REMOTE SENSING 13.5(2021).