Integration of support vector machines for hydrothermal alteration mapping using ASTER data – case study: the northwestern part of the Kerman Cenozoic Magmatic Arc, Iran
This work applies support vector machine (SVM) algorithms in two versions of singular and general SVM classifiers to map hydrothermal alteration zones in the northwestern part of the Kerman Cenozoic Magmatic Arc (KCMA). Three visible bands and six SWIR bands of ASTER images were applied as inputs fo...
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| Format: | Article |
| Language: | English |
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University of Tehran
2020-06-01
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| Series: | International Journal of Mining and Geo-Engineering |
| Subjects: | |
| Online Access: | https://ijmge.ut.ac.ir/article_75432_dc05630417bfdf47e8ee34b2854b5208.pdf |
| _version_ | 1818235933505355776 |
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| author | Saeed Mojeddifar Mohammad Mavadati |
| author_facet | Saeed Mojeddifar Mohammad Mavadati |
| author_sort | Saeed Mojeddifar |
| collection | DOAJ |
| description | This work applies support vector machine (SVM) algorithms in two versions of singular and general SVM classifiers to map hydrothermal alteration zones in the northwestern part of the Kerman Cenozoic Magmatic Arc (KCMA). Three visible bands and six SWIR bands of ASTER images were applied as inputs for SVM classifiers. The develosped algorithms were able to classify ASTER images into hydrothermal alteration or non-hydrothermal alteration classes. In singular SVM, nine classifiers were able to vote individually for every pixel in the image. Then, they were combined through integration rules to present a final decision about every pixel. The general SVM classifier integrated nine ASTER bands at the signal level to produce the final decision. The classification error rate showed that the general Gaussian RBF kernel-based SVM classifier had higher accuracy for the classification of hydrothermal alteration zones. The SVM results were then compared with other classified images based on band ratio and SAM methods. The main problem associated with these methods was that vegetation covering was highlighted as alteration zones while the SVM algorithm could solve this issue. Also, the verification of results, based on field and laboratory investigations, showed the SVM method to produce a more accurate map of alteration than that obtained from the band ratio and SAM. |
| first_indexed | 2024-12-12T12:01:50Z |
| format | Article |
| id | doaj.art-8acf2cfa394c4a0b96784125ec223e8c |
| institution | Directory Open Access Journal |
| issn | 2345-6949 |
| language | English |
| last_indexed | 2024-12-12T12:01:50Z |
| publishDate | 2020-06-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | International Journal of Mining and Geo-Engineering |
| spelling | doaj.art-8acf2cfa394c4a0b96784125ec223e8c2022-12-22T00:25:05ZengUniversity of TehranInternational Journal of Mining and Geo-Engineering2345-69492020-06-01541455010.22059/ijmge.2019.253801.59472575432Integration of support vector machines for hydrothermal alteration mapping using ASTER data – case study: the northwestern part of the Kerman Cenozoic Magmatic Arc, IranSaeed Mojeddifar0Mohammad Mavadati1Department of Mining Engineering, Arak University of Technology, Arak, IranDepartment of Electrical & Computer Engineering University of Denver, Denver, Co, USAThis work applies support vector machine (SVM) algorithms in two versions of singular and general SVM classifiers to map hydrothermal alteration zones in the northwestern part of the Kerman Cenozoic Magmatic Arc (KCMA). Three visible bands and six SWIR bands of ASTER images were applied as inputs for SVM classifiers. The develosped algorithms were able to classify ASTER images into hydrothermal alteration or non-hydrothermal alteration classes. In singular SVM, nine classifiers were able to vote individually for every pixel in the image. Then, they were combined through integration rules to present a final decision about every pixel. The general SVM classifier integrated nine ASTER bands at the signal level to produce the final decision. The classification error rate showed that the general Gaussian RBF kernel-based SVM classifier had higher accuracy for the classification of hydrothermal alteration zones. The SVM results were then compared with other classified images based on band ratio and SAM methods. The main problem associated with these methods was that vegetation covering was highlighted as alteration zones while the SVM algorithm could solve this issue. Also, the verification of results, based on field and laboratory investigations, showed the SVM method to produce a more accurate map of alteration than that obtained from the band ratio and SAM.https://ijmge.ut.ac.ir/article_75432_dc05630417bfdf47e8ee34b2854b5208.pdfhydrothermal alterationastersupport vector machineband ratiospectral angle mapper |
| spellingShingle | Saeed Mojeddifar Mohammad Mavadati Integration of support vector machines for hydrothermal alteration mapping using ASTER data – case study: the northwestern part of the Kerman Cenozoic Magmatic Arc, Iran International Journal of Mining and Geo-Engineering hydrothermal alteration aster support vector machine band ratio spectral angle mapper |
| title | Integration of support vector machines for hydrothermal alteration mapping using ASTER data – case study: the northwestern part of the Kerman Cenozoic Magmatic Arc, Iran |
| title_full | Integration of support vector machines for hydrothermal alteration mapping using ASTER data – case study: the northwestern part of the Kerman Cenozoic Magmatic Arc, Iran |
| title_fullStr | Integration of support vector machines for hydrothermal alteration mapping using ASTER data – case study: the northwestern part of the Kerman Cenozoic Magmatic Arc, Iran |
| title_full_unstemmed | Integration of support vector machines for hydrothermal alteration mapping using ASTER data – case study: the northwestern part of the Kerman Cenozoic Magmatic Arc, Iran |
| title_short | Integration of support vector machines for hydrothermal alteration mapping using ASTER data – case study: the northwestern part of the Kerman Cenozoic Magmatic Arc, Iran |
| title_sort | integration of support vector machines for hydrothermal alteration mapping using aster data case study the northwestern part of the kerman cenozoic magmatic arc iran |
| topic | hydrothermal alteration aster support vector machine band ratio spectral angle mapper |
| url | https://ijmge.ut.ac.ir/article_75432_dc05630417bfdf47e8ee34b2854b5208.pdf |
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