High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data
Generating a high-resolution whole-pixel geochemical contents map from a map with sparse distribution is a regression problem. Currently, multivariate prediction models like machine learning (ML) are constructed to raise the geoscience mapping resolution. Methods coupling the spatial autocorrelation...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-06-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/12/1991 |
| _version_ | 1827714451469500416 |
|---|---|
| author | Chenhui Huang Akinobu Shibuya |
| author_facet | Chenhui Huang Akinobu Shibuya |
| author_sort | Chenhui Huang |
| collection | DOAJ |
| description | Generating a high-resolution whole-pixel geochemical contents map from a map with sparse distribution is a regression problem. Currently, multivariate prediction models like machine learning (ML) are constructed to raise the geoscience mapping resolution. Methods coupling the spatial autocorrelation into the ML model have been proposed for raising ML prediction accuracy. Previously proposed methods are needed for complicated modification in ML models. In this research, we propose a new algorithm called spatial autocorrelation-based mixture interpolation (SABAMIN), with which it is easier to merge spatial autocorrelation into a ML model only using a data augmentation strategy. To test the feasibility of this concept, remote sensing data including those from the advanced spaceborne thermal emission and reflection radiometer (ASTER), digital elevation model (DEM), and geophysics (geomagnetic) data were used for the feasibility study, along with copper geochemical and copper mine data from Arizona, USA. We explained why spatial information can be coupled into an ML model only by data augmentation, and introduced how to operate data augmentation in our case. Four tests—(i) cross-validation of measured data, (ii) the blind test, (iii) the temporal stability test, and (iv) the predictor importance test—were conducted to evaluate the model. As the results, the model’s accuracy was improved compared with a traditional ML model, and the reliability of the algorithm was confirmed. In summary, combining the univariate interpolation method with multivariate prediction with data augmentation proved effective for geological studies. |
| first_indexed | 2024-03-10T18:58:52Z |
| format | Article |
| id | doaj.art-01ffaf40f0ac42738a0fcd5f736aba44 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T18:58:52Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-01ffaf40f0ac42738a0fcd5f736aba442023-11-20T04:31:01ZengMDPI AGRemote Sensing2072-42922020-06-011212199110.3390/rs12121991High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing DataChenhui Huang0Akinobu Shibuya1Biometrics Research Labs., NEC Corporation, 1131, Hinode, Abiko, Chiba 270-1174, JapanSystem Platform Research Labs., NEC Corporation, 1-1-1, Umezono, Tsukuba, Ibaraki 305-8568, JapanGenerating a high-resolution whole-pixel geochemical contents map from a map with sparse distribution is a regression problem. Currently, multivariate prediction models like machine learning (ML) are constructed to raise the geoscience mapping resolution. Methods coupling the spatial autocorrelation into the ML model have been proposed for raising ML prediction accuracy. Previously proposed methods are needed for complicated modification in ML models. In this research, we propose a new algorithm called spatial autocorrelation-based mixture interpolation (SABAMIN), with which it is easier to merge spatial autocorrelation into a ML model only using a data augmentation strategy. To test the feasibility of this concept, remote sensing data including those from the advanced spaceborne thermal emission and reflection radiometer (ASTER), digital elevation model (DEM), and geophysics (geomagnetic) data were used for the feasibility study, along with copper geochemical and copper mine data from Arizona, USA. We explained why spatial information can be coupled into an ML model only by data augmentation, and introduced how to operate data augmentation in our case. Four tests—(i) cross-validation of measured data, (ii) the blind test, (iii) the temporal stability test, and (iv) the predictor importance test—were conducted to evaluate the model. As the results, the model’s accuracy was improved compared with a traditional ML model, and the reliability of the algorithm was confirmed. In summary, combining the univariate interpolation method with multivariate prediction with data augmentation proved effective for geological studies.https://www.mdpi.com/2072-4292/12/12/1991geochemical mappingremote sensingmachine learningdata augmentationcomputational geometry |
| spellingShingle | Chenhui Huang Akinobu Shibuya High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data Remote Sensing geochemical mapping remote sensing machine learning data augmentation computational geometry |
| title | High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data |
| title_full | High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data |
| title_fullStr | High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data |
| title_full_unstemmed | High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data |
| title_short | High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data |
| title_sort | high accuracy geochemical map generation method by a spatial autocorrelation based mixture interpolation using remote sensing data |
| topic | geochemical mapping remote sensing machine learning data augmentation computational geometry |
| url | https://www.mdpi.com/2072-4292/12/12/1991 |
| work_keys_str_mv | AT chenhuihuang highaccuracygeochemicalmapgenerationmethodbyaspatialautocorrelationbasedmixtureinterpolationusingremotesensingdata AT akinobushibuya highaccuracygeochemicalmapgenerationmethodbyaspatialautocorrelationbasedmixtureinterpolationusingremotesensingdata |