Exploring Airborne LiDAR and Aerial Photographs Using Machine Learning for Land Cover Classification
Airborne LiDAR is a popular measurement technology in recent years. Its feature is that it can quickly acquire high precision and high density 3D point coordinates on the surface. The reflective waveform of the radar contains the geometric structure and roughness of the surface reflector. Combined w...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/9/2280 |
| _version_ | 1797601859852042240 |
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| author | Ming-Da Tsai Kuan-Wen Tseng Chia-Cheng Lai Chun-Ta Wei Ken-Fa Cheng |
| author_facet | Ming-Da Tsai Kuan-Wen Tseng Chia-Cheng Lai Chun-Ta Wei Ken-Fa Cheng |
| author_sort | Ming-Da Tsai |
| collection | DOAJ |
| description | Airborne LiDAR is a popular measurement technology in recent years. Its feature is that it can quickly acquire high precision and high density 3D point coordinates on the surface. The reflective waveform of the radar contains the geometric structure and roughness of the surface reflector. Combined with the information from aerial photographs, it can quickly help users to interpret various surface object types and serve as a basis for land cover classification. The experiment is divided into three phases. In the phase 1, LiDAR data and decision tree classification method (DT) were used to classify the land cover and customize the geometric parameter elevation. In the phase 2, we combined aerial photographs, LiDAR data and DT method to improve the accuracy of land cover classification. In the phase 3, the support vector machine classification method (SVM) was used to compare the classification accuracy of different classification methods. The results show that customizing the geometric parameter elevation can improve the overall classification accuracy. The results of the study showed that the DT method and the SVM method had better results for the grass, building and artificial ground, and the SVM method had better results for the planted shrub and bare ground. |
| first_indexed | 2024-03-11T04:08:24Z |
| format | Article |
| id | doaj.art-dcc4d6f153c941f18aea907bd714a9dd |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:08:24Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-dcc4d6f153c941f18aea907bd714a9dd2023-11-17T23:38:00ZengMDPI AGRemote Sensing2072-42922023-04-01159228010.3390/rs15092280Exploring Airborne LiDAR and Aerial Photographs Using Machine Learning for Land Cover ClassificationMing-Da Tsai0Kuan-Wen Tseng1Chia-Cheng Lai2Chun-Ta Wei3Ken-Fa Cheng4Department of Environmental Information and Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan 335009, TaiwanSchool of Defense Science, Chung Cheng Institute of Technology, National Defense University, Taoyuan 335009, TaiwanDepartment of Environmental Information and Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan 335009, TaiwanSchool of Defense Science, Chung Cheng Institute of Technology, National Defense University, Taoyuan 335009, TaiwanDepartment of Chemical and Materials Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan 335009, TaiwanAirborne LiDAR is a popular measurement technology in recent years. Its feature is that it can quickly acquire high precision and high density 3D point coordinates on the surface. The reflective waveform of the radar contains the geometric structure and roughness of the surface reflector. Combined with the information from aerial photographs, it can quickly help users to interpret various surface object types and serve as a basis for land cover classification. The experiment is divided into three phases. In the phase 1, LiDAR data and decision tree classification method (DT) were used to classify the land cover and customize the geometric parameter elevation. In the phase 2, we combined aerial photographs, LiDAR data and DT method to improve the accuracy of land cover classification. In the phase 3, the support vector machine classification method (SVM) was used to compare the classification accuracy of different classification methods. The results show that customizing the geometric parameter elevation can improve the overall classification accuracy. The results of the study showed that the DT method and the SVM method had better results for the grass, building and artificial ground, and the SVM method had better results for the planted shrub and bare ground.https://www.mdpi.com/2072-4292/15/9/2280LiDARdecision treesupport vector machineland cover classificationgeometric parameter |
| spellingShingle | Ming-Da Tsai Kuan-Wen Tseng Chia-Cheng Lai Chun-Ta Wei Ken-Fa Cheng Exploring Airborne LiDAR and Aerial Photographs Using Machine Learning for Land Cover Classification Remote Sensing LiDAR decision tree support vector machine land cover classification geometric parameter |
| title | Exploring Airborne LiDAR and Aerial Photographs Using Machine Learning for Land Cover Classification |
| title_full | Exploring Airborne LiDAR and Aerial Photographs Using Machine Learning for Land Cover Classification |
| title_fullStr | Exploring Airborne LiDAR and Aerial Photographs Using Machine Learning for Land Cover Classification |
| title_full_unstemmed | Exploring Airborne LiDAR and Aerial Photographs Using Machine Learning for Land Cover Classification |
| title_short | Exploring Airborne LiDAR and Aerial Photographs Using Machine Learning for Land Cover Classification |
| title_sort | exploring airborne lidar and aerial photographs using machine learning for land cover classification |
| topic | LiDAR decision tree support vector machine land cover classification geometric parameter |
| url | https://www.mdpi.com/2072-4292/15/9/2280 |
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