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A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables

Opened Access A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables

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Autor: García Gutiérrez, Jorge
Martínez Álvarez, Francisco
Troncoso Lora, Alicia
Riquelme Santos, José Cristóbal
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2015
Publicado en: Neurocomputing, 167, 24-31.
Tipo de documento: Artículo
Resumen: Light Detection and Ranging (LiDAR) is a remote sensor able to extract three-dimensional information. Environmental models in forest areas have been benefited by the use of LiDAR-derived information in the last years. A multiple linear regression (MLR) with previous stepwise feature selection is the most common method in the literature to develop those models. MLR defines the relation between the set of field measurements and the statistics extracted from a LiDAR flight. Machine learning has emerged as a suitable tool to improve classic stepwise MLR results on LiDAR. Unfortunately, few studies have been proposed to compare the quality of the multiple machine learning approaches. This paper presents a comparison between the classic MLR-based methodology and regression techniques in machine learning (neural networks, support vector machines, nearest neighbour, ensembles such as random forests) with special emphasis on regression trees. The selected techniques are applied to real...
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Cita: García Gutiérrez, J., Martínez Álvarez, F., Troncoso Lora, A. y Riquelme Santos, J.C. (2015). A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables. Neurocomputing, 167, 24-31.
Tamaño: 1.111Mb
Formato: PDF

URI: http://hdl.handle.net/11441/43592

DOI: http://dx.doi.org/10.1016/j.neucom.2014.09.091

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