A New Strategy to Fuse Remote Sensing Data and Geochemical Data with Different Machine Learning Methods
Geochemical data can reflect geological features, making it one of the basic types of geodata that have been widely used in mineral exploration, environmental assessment, resource potential analysis and other research. However, final decisions regarding activities are often limited by the spatial ac...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/4/930 |
| _version_ | 1797618468257792000 |
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| author | Shi Bai Jie Zhao |
| author_facet | Shi Bai Jie Zhao |
| author_sort | Shi Bai |
| collection | DOAJ |
| description | Geochemical data can reflect geological features, making it one of the basic types of geodata that have been widely used in mineral exploration, environmental assessment, resource potential analysis and other research. However, final decisions regarding activities are often limited by the spatial accuracy of geochemical data. Geochemical sampling is sometimes difficult to conduct because of harsh natural and geographic conditions (e.g., mountainous areas with high altitude and complex terrain), meaning that only medium/low-precision survey data could be obtained, which may not be adequate for regional geochemical mapping and exploration. Modern techniques such as remote sensing could be used to address this issue. In recent decades, the development of remote sensing technology has provided a huge amount of earth observation data with high spatial, temporal and spectral resolutions. The advantage of rapid acquisition of spatial and spectral information of large areas has promoted the broad use of remote sensing data in geoscientific research. Remote sensing data can help to differentiate various ground features by recording the electromagnetic response of the surface to solar radiation. Many problems that occur during the process of fusing remote sensing and geochemical data have been reported, such as the feasibility of existing fusion methods and low fusion accuracies that are less useful in practice. In this paper, a new strategy for integrating geochemical data and remote sensing data (referred to as ASTER data) is proposed; this strategy is achieved through linear regression as well as random forest and support vector regression algorithms. The results show that support vector regression can obtain better results for the available data sets and prove that the strategy currently proposed can effectively support the fusion of high-spatial-resolution remote sensing data (15 m) and low-spatial-resolution geochemical data (2000 m) in wide-range accurate geochemical applications (e.g., lithological identification and geochemical exploration). |
| first_indexed | 2024-03-11T08:13:17Z |
| format | Article |
| id | doaj.art-e1a414689d1f4fffa587ee5d249331c7 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T08:13:17Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e1a414689d1f4fffa587ee5d249331c72023-11-16T23:01:25ZengMDPI AGRemote Sensing2072-42922023-02-0115493010.3390/rs15040930A New Strategy to Fuse Remote Sensing Data and Geochemical Data with Different Machine Learning MethodsShi Bai0Jie Zhao1School of Earth Sciences and Resources, China University of Geoscience, Beijing 100083, ChinaSchool of Earth Sciences and Resources, China University of Geoscience, Beijing 100083, ChinaGeochemical data can reflect geological features, making it one of the basic types of geodata that have been widely used in mineral exploration, environmental assessment, resource potential analysis and other research. However, final decisions regarding activities are often limited by the spatial accuracy of geochemical data. Geochemical sampling is sometimes difficult to conduct because of harsh natural and geographic conditions (e.g., mountainous areas with high altitude and complex terrain), meaning that only medium/low-precision survey data could be obtained, which may not be adequate for regional geochemical mapping and exploration. Modern techniques such as remote sensing could be used to address this issue. In recent decades, the development of remote sensing technology has provided a huge amount of earth observation data with high spatial, temporal and spectral resolutions. The advantage of rapid acquisition of spatial and spectral information of large areas has promoted the broad use of remote sensing data in geoscientific research. Remote sensing data can help to differentiate various ground features by recording the electromagnetic response of the surface to solar radiation. Many problems that occur during the process of fusing remote sensing and geochemical data have been reported, such as the feasibility of existing fusion methods and low fusion accuracies that are less useful in practice. In this paper, a new strategy for integrating geochemical data and remote sensing data (referred to as ASTER data) is proposed; this strategy is achieved through linear regression as well as random forest and support vector regression algorithms. The results show that support vector regression can obtain better results for the available data sets and prove that the strategy currently proposed can effectively support the fusion of high-spatial-resolution remote sensing data (15 m) and low-spatial-resolution geochemical data (2000 m) in wide-range accurate geochemical applications (e.g., lithological identification and geochemical exploration).https://www.mdpi.com/2072-4292/15/4/930remote sensinggeochemicalmachine learninglinear regressiondata fusion |
| spellingShingle | Shi Bai Jie Zhao A New Strategy to Fuse Remote Sensing Data and Geochemical Data with Different Machine Learning Methods Remote Sensing remote sensing geochemical machine learning linear regression data fusion |
| title | A New Strategy to Fuse Remote Sensing Data and Geochemical Data with Different Machine Learning Methods |
| title_full | A New Strategy to Fuse Remote Sensing Data and Geochemical Data with Different Machine Learning Methods |
| title_fullStr | A New Strategy to Fuse Remote Sensing Data and Geochemical Data with Different Machine Learning Methods |
| title_full_unstemmed | A New Strategy to Fuse Remote Sensing Data and Geochemical Data with Different Machine Learning Methods |
| title_short | A New Strategy to Fuse Remote Sensing Data and Geochemical Data with Different Machine Learning Methods |
| title_sort | new strategy to fuse remote sensing data and geochemical data with different machine learning methods |
| topic | remote sensing geochemical machine learning linear regression data fusion |
| url | https://www.mdpi.com/2072-4292/15/4/930 |
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