Classification of landcover from combined LiDAR and orthophotos using support vector machine
College
College of Computer Studies
Department/Unit
Information Technology
Document Type
Conference Proceeding
Source Title
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019
Publication Date
11-1-2019
Abstract
© 2019 IEEE. The study is based on the Landcover classification from combined light detection and ranging (LiDAR) data and orthophotos. Five land classes were extracted namely: barren, build up, low vegetation, mango, and non-agricultural trees. Support vector machine (SVM) was the algorithm used for the classification. Different LiDAR derivatives and orthophoto were used as an input which are intensity, digital terrain model (DTM), digital surface model (DSM), normalized digital surface model (NDSM), and RGB combination of orthophotos. The applied algorithm has 100% accuracy based on the confusion matrix which means that SVM is a good algorithm in classification of landcover from combined LiDAR and orthophotos given that the right LiDAR derivatives were used.
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Digitial Object Identifier (DOI)
10.1109/HNICEM48295.2019.9073587
Recommended Citation
Pula, R. A., Concepcion, R., Ilagan, L., & Tobias, R. (2019). Classification of landcover from combined LiDAR and orthophotos using support vector machine. 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019 https://doi.org/10.1109/HNICEM48295.2019.9073587
Keywords
Land cover—Classification; Support vector machines; Orthophotography; Digital elevation models
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