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Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids

College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
Key Laboratory of Agricultural Land Quality and Monitoring, Ministry of Natural Resources, Beijing 100083, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(12), 1500;
Received: 22 April 2019 / Revised: 21 June 2019 / Accepted: 21 June 2019 / Published: 25 June 2019
Large-scale crop mapping provides important information in agricultural applications. However, it is a challenging task due to the inconsistent availability of remote sensing data caused by the irregular time series and limited coverage of the images, together with the low spatial resolution of the classification results. In this study, we proposed a new efficient method based on grids to address the inconsistent availability of the high-medium resolution images for large-scale crop classification. First, we proposed a method to block the remote sensing data into grids to solve the problem of temporal inconsistency. Then, a parallel computing technique was introduced to improve the calculation efficiency on the grid scale. Experiments were designed to evaluate the applicability of this method for different high-medium spatial resolution remote sensing images and different machine learning algorithms and to compare the results with the widely used nonparallel method. The computational experiments showed that the proposed method was successful at identifying large-scale crop distribution using common high-medium resolution remote sensing images (GF-1 WFV images and Sentinel-2) and common machine learning classifiers (the random forest algorithm and support vector machine). Finally, we mapped the croplands in Heilongjiang Province in 2015, 2016, 2017, which used a random forest classifier with the time series GF-1 WFV images spectral features, the enhanced vegetation index (EVI) and normalized difference water index (NDWI). Ultimately, the accuracy was assessed using a confusion matrix. The results showed that the classification accuracy reached 88%, 82%, and 85% in 2015, 2016, and 2017, respectively. In addition, with the help of parallel computing, the calculation speed was significantly improved by at least seven-fold. This indicates that using the grid framework to block the data for classification is feasible for crop mapping in large areas and has great application potential in the future. View Full-Text
Keywords: large scale; crop mapping; random forest; grid; parallel large scale; crop mapping; random forest; grid; parallel
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MDPI and ACS Style

Yang, N.; Liu, D.; Feng, Q.; Xiong, Q.; Zhang, L.; Ren, T.; Zhao, Y.; Zhu, D.; Huang, J. Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids. Remote Sens. 2019, 11, 1500.

AMA Style

Yang N, Liu D, Feng Q, Xiong Q, Zhang L, Ren T, Zhao Y, Zhu D, Huang J. Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids. Remote Sensing. 2019; 11(12):1500.

Chicago/Turabian Style

Yang, Ning, Diyou Liu, Quanlong Feng, Quan Xiong, Lin Zhang, Tianwei Ren, Yuanyuan Zhao, Dehai Zhu, and Jianxi Huang. 2019. "Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids" Remote Sensing 11, no. 12: 1500.

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