Article
A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables
Author/s | García Gutiérrez, Jorge
Martínez Álvarez, Francisco Troncoso Lora, Alicia Riquelme Santos, José Cristóbal |
Department | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Publication Date | 2015 |
Deposit Date | 2016-07-14 |
Published in |
|
Abstract | 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 ... 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 LiDAR data from two areas in the province of Lugo (Galizia, Spain). The results confirm that classic MLR is outperformed by machine learning techniques and concretely, our experiments suggest that Support Vector Regression with Gaussian kernels statistically outperforms the rest of the techniques. |
Project ID. | TIN2011-28956-C02
P12- TIC-1728 APPB813097 |
Citation | 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. |
Files | Size | Format | View | Description |
---|---|---|---|---|
A comparison of machine.pdf | 1.111Mb | [PDF] | View/ | |