COMPARISON OF OBJECT BASED MACHINE LEARNING CLASSIFICATIONS OF PLANETSCOPE AND WORLDVIEW-3 SATELLITE IMAGES FOR LAND USE / COVER
The purpose of the study was to compare performance of the classification methods, that are Rule Based (RB) classifier and Support Vector Machine (SVM), of Planetscope and Worldview-3 satellite images in order to produce land use / cover thematic maps. Six classes, which are deep water, shallow wate...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2019-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/1887/2019/isprs-archives-XLII-2-W13-1887-2019.pdf |
| _version_ | 1818472180574322688 |
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| author | A. Tuzcu G. Taskin N. Musaoğlu |
| author_facet | A. Tuzcu G. Taskin N. Musaoğlu |
| author_sort | A. Tuzcu |
| collection | DOAJ |
| description | The purpose of the study was to compare performance of the classification methods, that are Rule Based (RB) classifier and Support Vector Machine (SVM), of Planetscope and Worldview-3 satellite images in order to produce land use / cover thematic maps. Six classes, which are deep water, shallow water, vegetation, agricultural area, soil and saline soil, were considered. After performing the classification process, accuracy assessment was employed based on the error matrices. The results showed that, both of the classification methods and satellite data were adequate to classify the area. Besides, classification accuracy was improved when Worldview-3 satellite and SVM method were used. The classification accuracies of RB classification of Planetscope and Worldview-3 were %87 and %94 respectively and the classification accuracies of SVM classification of Planetscope and Worldview-3 were %93 and %96 respectively. |
| first_indexed | 2024-04-14T04:02:04Z |
| format | Article |
| id | doaj.art-eef86ab0eced4e5dbf6c53229a78ad08 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-14T04:02:04Z |
| publishDate | 2019-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-eef86ab0eced4e5dbf6c53229a78ad082022-12-22T02:13:30ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342019-06-01XLII-2-W131887189210.5194/isprs-archives-XLII-2-W13-1887-2019COMPARISON OF OBJECT BASED MACHINE LEARNING CLASSIFICATIONS OF PLANETSCOPE AND WORLDVIEW-3 SATELLITE IMAGES FOR LAND USE / COVERA. Tuzcu0G. Taskin1N. Musaoğlu2Istanbul Technical University, Faculty of Civil Engineering, Department of Geomatics Engineering, 34469 Maslak, Istanbul, TurkeyIstanbul Technical University, Earthquake Engineering and Disaster Management Institute, 34469 Maslak, Istanbul, TurkeyIstanbul Technical University, Faculty of Civil Engineering, Department of Geomatics Engineering, 34469 Maslak, Istanbul, TurkeyThe purpose of the study was to compare performance of the classification methods, that are Rule Based (RB) classifier and Support Vector Machine (SVM), of Planetscope and Worldview-3 satellite images in order to produce land use / cover thematic maps. Six classes, which are deep water, shallow water, vegetation, agricultural area, soil and saline soil, were considered. After performing the classification process, accuracy assessment was employed based on the error matrices. The results showed that, both of the classification methods and satellite data were adequate to classify the area. Besides, classification accuracy was improved when Worldview-3 satellite and SVM method were used. The classification accuracies of RB classification of Planetscope and Worldview-3 were %87 and %94 respectively and the classification accuracies of SVM classification of Planetscope and Worldview-3 were %93 and %96 respectively.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/1887/2019/isprs-archives-XLII-2-W13-1887-2019.pdf |
| spellingShingle | A. Tuzcu G. Taskin N. Musaoğlu COMPARISON OF OBJECT BASED MACHINE LEARNING CLASSIFICATIONS OF PLANETSCOPE AND WORLDVIEW-3 SATELLITE IMAGES FOR LAND USE / COVER The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | COMPARISON OF OBJECT BASED MACHINE LEARNING CLASSIFICATIONS OF PLANETSCOPE AND WORLDVIEW-3 SATELLITE IMAGES FOR LAND USE / COVER |
| title_full | COMPARISON OF OBJECT BASED MACHINE LEARNING CLASSIFICATIONS OF PLANETSCOPE AND WORLDVIEW-3 SATELLITE IMAGES FOR LAND USE / COVER |
| title_fullStr | COMPARISON OF OBJECT BASED MACHINE LEARNING CLASSIFICATIONS OF PLANETSCOPE AND WORLDVIEW-3 SATELLITE IMAGES FOR LAND USE / COVER |
| title_full_unstemmed | COMPARISON OF OBJECT BASED MACHINE LEARNING CLASSIFICATIONS OF PLANETSCOPE AND WORLDVIEW-3 SATELLITE IMAGES FOR LAND USE / COVER |
| title_short | COMPARISON OF OBJECT BASED MACHINE LEARNING CLASSIFICATIONS OF PLANETSCOPE AND WORLDVIEW-3 SATELLITE IMAGES FOR LAND USE / COVER |
| title_sort | comparison of object based machine learning classifications of planetscope and worldview 3 satellite images for land use cover |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/1887/2019/isprs-archives-XLII-2-W13-1887-2019.pdf |
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