Identifying Emeishan basalt by supervised learning with Landsat-5 and ASTER data
Multispectral-sensor images are advantageous in terms of discriminating major lithologies due to their high spatial resolution and intermediate spectral resolution, in addition to their low cost and high accessibility in comparison to hyperspectral images. In this study, Landsat-5 Thematic Mapper ™...
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.1097778/full |
| _version_ | 1797957676551897088 |
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| author | Ling Zeng Tianbin Li Haitao Huang Peng Zeng Yuanxiao He Linhai Jing Yan Yang Shoutao Jiao |
| author_facet | Ling Zeng Tianbin Li Haitao Huang Peng Zeng Yuanxiao He Linhai Jing Yan Yang Shoutao Jiao |
| author_sort | Ling Zeng |
| collection | DOAJ |
| description | Multispectral-sensor images are advantageous in terms of discriminating major lithologies due to their high spatial resolution and intermediate spectral resolution, in addition to their low cost and high accessibility in comparison to hyperspectral images. In this study, Landsat-5 Thematic Mapper ™ and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data—which are the most widely used multispectral data for the discrimination of the mixed rock units—are utilized to identify basalts in our study area. Further, prior knowledge regarding basalt-distribution areas in our study region is obtained from the geological-survey results conducted by the Sichuan Geological Survey at 2005, which is used as the reference of correction to assess our identified results. Small portions of this prior area of basalt distribution were verified through field checks, which were then determined as sites for use as training data for remote-sensing imagery. Three supervised-classification algorithms within ENVI 5.3—k-nearest neighbors (KNN), maximum likelihood classification (MLC), and support vertical machine (SVM)—were utilized for model identification. As a result, six models were constructed, including the KNN prediction of basalts by ASTER images, SVM prediction by ASTER, MLC prediction by ASTER, KNN prediction by Landsat-5 images, SVM prediction by Landsat-5, and MLC prediction by Landsat-5. The performances of the six models, in terms of precision and accuracy, show that the optimum model is Landsat-5 by SVM, with a precision of 70.92% and accuracy of 99.97%, followed by the ASTER by SVM model, with a precision of 67.72% and accuracy of 99.89% and the Landsat-5 by KNN model, with a precision of 57.23% and accuracy of 99.85%. |
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| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-11T00:07:32Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-3f8e59f2ef6f42328f3be59b030856ee2023-01-09T09:30:49ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-01-011010.3389/feart.2022.10977781097778Identifying Emeishan basalt by supervised learning with Landsat-5 and ASTER dataLing Zeng0Tianbin Li1Haitao Huang2Peng Zeng3Yuanxiao He4Linhai Jing5Yan Yang6Shoutao Jiao7Geomathematics Key Laboratory of Sichuan Province, College of Mathematics and Physics, Chengdu University of Technology, Chengdu, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, ChinaCollege of Earth Sciences, Chengdu University of Technology, Chengdu, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, ChinaSichuan Geological Survey, Chengdu, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaDevelopment Research Center of China Geological Survey, Beijing, ChinaDevelopment Research Center of China Geological Survey, Beijing, ChinaMultispectral-sensor images are advantageous in terms of discriminating major lithologies due to their high spatial resolution and intermediate spectral resolution, in addition to their low cost and high accessibility in comparison to hyperspectral images. In this study, Landsat-5 Thematic Mapper ™ and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data—which are the most widely used multispectral data for the discrimination of the mixed rock units—are utilized to identify basalts in our study area. Further, prior knowledge regarding basalt-distribution areas in our study region is obtained from the geological-survey results conducted by the Sichuan Geological Survey at 2005, which is used as the reference of correction to assess our identified results. Small portions of this prior area of basalt distribution were verified through field checks, which were then determined as sites for use as training data for remote-sensing imagery. Three supervised-classification algorithms within ENVI 5.3—k-nearest neighbors (KNN), maximum likelihood classification (MLC), and support vertical machine (SVM)—were utilized for model identification. As a result, six models were constructed, including the KNN prediction of basalts by ASTER images, SVM prediction by ASTER, MLC prediction by ASTER, KNN prediction by Landsat-5 images, SVM prediction by Landsat-5, and MLC prediction by Landsat-5. The performances of the six models, in terms of precision and accuracy, show that the optimum model is Landsat-5 by SVM, with a precision of 70.92% and accuracy of 99.97%, followed by the ASTER by SVM model, with a precision of 67.72% and accuracy of 99.89% and the Landsat-5 by KNN model, with a precision of 57.23% and accuracy of 99.85%.https://www.frontiersin.org/articles/10.3389/feart.2022.1097778/fullidentifying basaltLandsat-5ASTERsupervised learningmachine-learning algorithms |
| spellingShingle | Ling Zeng Tianbin Li Haitao Huang Peng Zeng Yuanxiao He Linhai Jing Yan Yang Shoutao Jiao Identifying Emeishan basalt by supervised learning with Landsat-5 and ASTER data Frontiers in Earth Science identifying basalt Landsat-5 ASTER supervised learning machine-learning algorithms |
| title | Identifying Emeishan basalt by supervised learning with Landsat-5 and ASTER data |
| title_full | Identifying Emeishan basalt by supervised learning with Landsat-5 and ASTER data |
| title_fullStr | Identifying Emeishan basalt by supervised learning with Landsat-5 and ASTER data |
| title_full_unstemmed | Identifying Emeishan basalt by supervised learning with Landsat-5 and ASTER data |
| title_short | Identifying Emeishan basalt by supervised learning with Landsat-5 and ASTER data |
| title_sort | identifying emeishan basalt by supervised learning with landsat 5 and aster data |
| topic | identifying basalt Landsat-5 ASTER supervised learning machine-learning algorithms |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.1097778/full |
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