Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification

doi:10.3390/rs11141665

	Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification
	He, Tianle 1,2; Xie, Chuanjie 2; Liu, Qingsheng 2; Guan, Shiying 2,3; Liu, Gaohuan 2
刊名	REMOTE SENSING
	2019-07-02
卷号	11 期号:14 页码:21
关键词	winter wheat identification random forest A-LSTM pixel-mixing effect variable importance analysis
ISSN号	2072-4292
DOI	10.3390/rs11141665
通讯作者	Xie, Chuanjie(xiecj@lreis.ac.cn)
英文摘要	Machine learning comprises a group of powerful state-of-the-art techniques for land cover classification and cropland identification. In this paper, we proposed and evaluated two models based on random forest (RF) and attention-based long short-term memory (A-LSTM) networks that can learn directly from the raw surface reflectance of remote sensing (RS) images for large-scale winter wheat identification in Huanghuaihai Region (North-Central China). We used a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images over one growing season and the corresponding winter wheat distribution map for the experiments. Each training sample was derived from the raw surface reflectance of MODIS time-series images. Both models achieved state-of-the-art performance in identifying winter wheat, and the F1 scores of RF and A-LSTM were 0.72 and 0.71, respectively. We also analyzed the impact of the pixel-mixing effect. Training with pure-mixed-pixel samples (the training set consists of pure and mixed cells and thus retains the original distribution of data) was more precise than training with only pure-pixel samples (the entire pixel area belongs to one class). We also analyzed the variable importance along the temporal series, and the data acquired in March or April contributed more than the data acquired at other times. Both models could predict winter wheat coverage in past years or in other regions with similar winter wheat growing seasons. The experiments in this paper showed the effectiveness and significance of our methods.
资助项目	National Key Research and Development Program of China[2017YFD0300403] ; Laboratory Independent Innovation Project of State Key Laboratory of Resources and Environment Information System
WOS关键词	IMAGE TIME-SERIES ; LAND-COVER ; PERFORMANCE
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000480527800035
资助机构	National Key Research and Development Program of China ; Laboratory Independent Innovation Project of State Key Laboratory of Resources and Environment Information System
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/68932]
专题	中国科学院地理科学与资源研究所
通讯作者	Xie, Chuanjie
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Henan Polytech Univ, Jiaozuo 454000, Henan, Peoples R China
推荐引用方式 GB/T 7714	He, Tianle,Xie, Chuanjie,Liu, Qingsheng,et al. Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification[J]. REMOTE SENSING,2019,11(14):21.
APA	He, Tianle,Xie, Chuanjie,Liu, Qingsheng,Guan, Shiying,&Liu, Gaohuan.(2019).Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification.REMOTE SENSING,11(14),21.
MLA	He, Tianle,et al."Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification".REMOTE SENSING 11.14(2019):21.