A comparison of pixel-based decision tree and object-based Support Vector Machine methods for land-cover classification based on aerial images and airborne lidar data

Qiong WuBeijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China;College of Resource Environment and Tourism, Capital Normal University, Beijing, ChinaView further author information

Ruofei ZhongBeijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China;College of Resource Environment and Tourism, Capital Normal University, Beijing, ChinaCorrespondence[email protected]
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Wenji ZhaoBeijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China;College of Resource Environment and Tourism, Capital Normal University, Beijing, ChinaView further author information

Han FuBeijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China;College of Resource Environment and Tourism, Capital Normal University, Beijing, ChinaView further author information

Kai SongBeijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China;College of Resource Environment and Tourism, Capital Normal University, Beijing, ChinaView further author information

A comparison of pixel-based decision tree and object-based Support Vector Machine methods for land-cover classification based on aerial images and airborne lidar data

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A comparison of pixel-based decision tree and object-based Support Vector Machine methods for land-cover classification based on aerial images and airborne lidar data

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