Maximizing the Diversity of Ensemble Random Forests for Tree Genera Classification Using High Density LiDAR Data

Maximizing the Diversity of Ensemble Random Forests for Tree Genera Classification Using High Density LiDAR Data

Recent research into improving the effectiveness of forest inventory management using airborne LiDAR data has focused on developing advanced theories in data analytics. Furthermore, supervised learning as a predictive model for classifying tree genera (and species, where possible) has been gaining p...

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Bibliographic Details
Main Authors: Connie Ko, Gunho Sohn, Tarmo K. Remmel, John R. Miller
Format: Article
Language:English
Published: MDPI AG 2016-08-01
Series:Remote Sensing
Subjects:
tree genera classification
LiDAR
random forests
ensemble classification
diversity maximization
Online Access:http://www.mdpi.com/2072-4292/8/8/646

Internet

http://www.mdpi.com/2072-4292/8/8/646

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