Abstract
Wetlands have received intensive interdisciplinary attention as a unique ecosystem and valuable resources. As a new technology, the airborne LiDAR system has been applied in wetland research these years. However, most of the studies used only one or two LiDAR observations to extract either terrain or vegetation in wetlands. This research aims at integrating LiDAR's multiple attributes (DSM, DTM, off-ground features, Slop map, multiple pulse returns, and normalized intensity) to improve mapping and classification of wetlands based on a multi-level object-oriented classification method. By using this method, we are able to classify the Yellow River Delta wetland into eight classes with overall classification accuracy of 92.5%
Original language | English |
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Title of host publication | 2nd ISPRS International Conference on Computer Vision in Remote Sensing, CVRS 2015 |
Publisher | SPIE |
Volume | 9901 |
ISBN (Electronic) | 9781510601543 |
DOIs | |
Publication status | Published - 1 Jan 2016 |
Event | 2nd ISPRS International Conference on Computer Vision in Remote Sensing, CVRS 2015 - Xiamen, China Duration: 28 Apr 2015 → 30 Apr 2015 |
Conference
Conference | 2nd ISPRS International Conference on Computer Vision in Remote Sensing, CVRS 2015 |
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Country/Territory | China |
City | Xiamen |
Period | 28/04/15 → 30/04/15 |
Keywords
- accuracy
- classification
- LiDAR
- multiple attributes
- wetlands
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering