Analysis on Effective UAS Survey Conditions for Classification of Coastal Sediments
This study aims to introduce effective unmanned aerial system (UAS) survey conditions for coastal sediment classification, including muddy sand, sand, gravel, and shells in a tidal flat area. UAS images with resolutions ranging from 2 to 60 mm are used as an implication of survey altitudes. The UAS...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9658160/ |
| _version_ | 1797947287013425152 |
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| author | Hyesu Kim Jaehyung Yu Lei Wang Chanhyeok Park Hyuk Soo Han Seong-Geon Jang |
| author_facet | Hyesu Kim Jaehyung Yu Lei Wang Chanhyeok Park Hyuk Soo Han Seong-Geon Jang |
| author_sort | Hyesu Kim |
| collection | DOAJ |
| description | This study aims to introduce effective unmanned aerial system (UAS) survey conditions for coastal sediment classification, including muddy sand, sand, gravel, and shells in a tidal flat area. UAS images with resolutions ranging from 2 to 60 mm are used as an implication of survey altitudes. The UAS images are used for sediment classification using random forest (RF) and support vector machine (SVM) methods. The results showed that RF is more effective in sediment classification while the general accuracy pattern was similar. The accuracy decreased with lower spatial resolutions. Notably, there is a significant drop of accuracy with a resolution coarser than 40 mm. Considering the training data selection, classification accuracy, and survey efficiency, it is suggested that 40 mm UAS images would provide optimal condition with acceptable accuracy for coastal sediment classification using RF model. To gain higher accuracy, a lower flight altitude is required, which will elongate the survey time significantly. Given the fact that this study is the first approach to test various UAS survey conditions for coastal sediment classifications in a field condition; the methodology and findings of this study can serve as a guideline framework for future coastal UAS sediment mapping. |
| first_indexed | 2024-04-10T21:25:25Z |
| format | Article |
| id | doaj.art-c92e6e0086c649b0920eca71a7ed3e95 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-10T21:25:25Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-c92e6e0086c649b0920eca71a7ed3e952023-01-20T00:00:20ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-01151163117310.1109/JSTARS.2021.31362289658160Analysis on Effective UAS Survey Conditions for Classification of Coastal SedimentsHyesu Kim0https://orcid.org/0000-0002-4639-0273Jaehyung Yu1https://orcid.org/0000-0002-4518-2923Lei Wang2https://orcid.org/0000-0003-1298-4839Chanhyeok Park3Hyuk Soo Han4Seong-Geon Jang5Department of Astronomy Space Science and Geology, Chungnam National University, Daejeon, KoreaDepartment of Geology and Earth Environmental Sciences, Chungnam National University, Daejeon, South KoreaDepartment of Geography & Anthropology, Louisiana State University, Baton Rouge, LA, USADepartment of Astronomy Space Science and Geology, Chungnam National University, Daejeon, KoreaKorea Marineaid Company Ltd., Daejeon, KoreaMarine Research Center, National Park Research Institute, Korea National Park Service (KNPS), Yeosu, KoreaThis study aims to introduce effective unmanned aerial system (UAS) survey conditions for coastal sediment classification, including muddy sand, sand, gravel, and shells in a tidal flat area. UAS images with resolutions ranging from 2 to 60 mm are used as an implication of survey altitudes. The UAS images are used for sediment classification using random forest (RF) and support vector machine (SVM) methods. The results showed that RF is more effective in sediment classification while the general accuracy pattern was similar. The accuracy decreased with lower spatial resolutions. Notably, there is a significant drop of accuracy with a resolution coarser than 40 mm. Considering the training data selection, classification accuracy, and survey efficiency, it is suggested that 40 mm UAS images would provide optimal condition with acceptable accuracy for coastal sediment classification using RF model. To gain higher accuracy, a lower flight altitude is required, which will elongate the survey time significantly. Given the fact that this study is the first approach to test various UAS survey conditions for coastal sediment classifications in a field condition; the methodology and findings of this study can serve as a guideline framework for future coastal UAS sediment mapping.https://ieeexplore.ieee.org/document/9658160/Classificationcoastal sedimentssurvey conditionUAS |
| spellingShingle | Hyesu Kim Jaehyung Yu Lei Wang Chanhyeok Park Hyuk Soo Han Seong-Geon Jang Analysis on Effective UAS Survey Conditions for Classification of Coastal Sediments IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Classification coastal sediments survey condition UAS |
| title | Analysis on Effective UAS Survey Conditions for Classification of Coastal Sediments |
| title_full | Analysis on Effective UAS Survey Conditions for Classification of Coastal Sediments |
| title_fullStr | Analysis on Effective UAS Survey Conditions for Classification of Coastal Sediments |
| title_full_unstemmed | Analysis on Effective UAS Survey Conditions for Classification of Coastal Sediments |
| title_short | Analysis on Effective UAS Survey Conditions for Classification of Coastal Sediments |
| title_sort | analysis on effective uas survey conditions for classification of coastal sediments |
| topic | Classification coastal sediments survey condition UAS |
| url | https://ieeexplore.ieee.org/document/9658160/ |
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