Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data

Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data

Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been developed and incorporated into the machine learnin...

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Bibliographic Details
Main Authors: Sanjiwana Arjasakusuma, Sandiaga Swahyu Kusuma, Stuart Phinn
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:ISPRS International Journal of Geo-Information
Subjects:
forest height
feature selection
dimensionality reduction
lidar
AISA hyperspectral
Online Access:https://www.mdpi.com/2220-9964/9/9/507

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