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

1
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Henan Polytechnic University, Jiaozuo 454000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(14), 1665; https://doi.org/10.3390/rs11141665
Received: 29 May 2019 / Revised: 3 July 2019 / Accepted: 7 July 2019 / Published: 12 July 2019
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Abstract

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. View Full-Text
Keywords: winter wheat identification; random forest; A-LSTM; pixel-mixing effect; variable importance analysis winter wheat identification; random forest; A-LSTM; pixel-mixing effect; variable importance analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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He, T.; Xie, C.; Liu, Q.; Guan, S.; Liu, G. Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification. Remote Sens. 2019, 11, 1665.

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