Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids
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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/12/1500 |
| _version_ | 1818960928628015104 |
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| author | Ning Yang Diyou Liu Quanlong Feng Quan Xiong Lin Zhang Tianwei Ren Yuanyuan Zhao Dehai Zhu Jianxi Huang |
| author_facet | Ning Yang Diyou Liu Quanlong Feng Quan Xiong Lin Zhang Tianwei Ren Yuanyuan Zhao Dehai Zhu Jianxi Huang |
| author_sort | Ning Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T12:05:20Z |
| format | Article |
| id | doaj.art-bdba763c783f4a4fad25a4b96c5cbdc1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T12:05:20Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-bdba763c783f4a4fad25a4b96c5cbdc12022-12-21T19:41:25ZengMDPI AGRemote Sensing2072-42922019-06-011112150010.3390/rs11121500rs11121500Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with GridsNing Yang0Diyou Liu1Quanlong Feng2Quan Xiong3Lin Zhang4Tianwei Ren5Yuanyuan Zhao6Dehai Zhu7Jianxi Huang8College of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaLarge-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.https://www.mdpi.com/2072-4292/11/12/1500large scalecrop mappingrandom forestgridparallel |
| spellingShingle | Ning Yang Diyou Liu Quanlong Feng Quan Xiong Lin Zhang Tianwei Ren Yuanyuan Zhao Dehai Zhu Jianxi Huang Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids Remote Sensing large scale crop mapping random forest grid parallel |
| title | Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids |
| title_full | Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids |
| title_fullStr | Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids |
| title_full_unstemmed | Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids |
| title_short | Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids |
| title_sort | large scale crop mapping based on machine learning and parallel computation with grids |
| topic | large scale crop mapping random forest grid parallel |
| url | https://www.mdpi.com/2072-4292/11/12/1500 |
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