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Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification
He, Tianle1,2; Xie, Chuanjie1; Liu, Qingsheng1; Guan, Shiying1,3; Liu, Gaohuan1
2019-07-02
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume11Issue:14Pages:21
Corresponding AuthorXie, Chuanjie(xiecj@lreis.ac.cn)
AbstractMachine 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.
Keywordwinter wheat identification random forest A-LSTM pixel-mixing effect variable importance analysis
DOI10.3390/rs11141665
WOS KeywordIMAGE TIME-SERIES ; LAND-COVER ; PERFORMANCE
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFD0300403] ; Laboratory Independent Innovation Project of State Key Laboratory of Resources and Environment Information System
Funding OrganizationNational Key Research and Development Program of China ; Laboratory Independent Innovation Project of State Key Laboratory of Resources and Environment Information System
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000480527800035
PublisherMDPI
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/68933
Collection中国科学院地理科学与资源研究所
Corresponding AuthorXie, Chuanjie
Affiliation1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Henan Polytech Univ, Jiaozuo 454000, Henan, Peoples R China
Recommended Citation
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.
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