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Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification
2019
Remote Sensing
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
doi:10.3390/rs11141665
fatcat:tih32y6d5rbo7gkmj6ijznkrda