A Comparative Study of Machine Learning Regression Methods on LiDAR Data: A Case Study
Light Detection and Ranging (LiDAR) is a remote sensor able to extract vertical information from sensed objects. LiDAR-derived information is nowadays used to develop environmental models for describing fire behaviour or quantifying biomass stocks in forest areas. A multiple linear regression (MLR) with previous stepwise feature selection is the most common method in the literature to develop LiDAR-derived models. MLR defines the relation between the set of field measurements and the statistics extracted from a LiDAR flight. Machine learning has recently been paid an increasing attention to improve classic MLR results. 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 common regression techniques in machine learning (neural networks, regression trees, support vector machines, nearest neighbour, and ensembles such as random forests). The selected techniques are applied to real LiDAR data from two areas in the province of Lugo (Galizia, Spain). The results show that support vector regression statistically outperforms the rest of techniques when feature selection is applied. However, its performance cannot be said statistically different from that of Random Forests when previous feature selection is skipped.
KeywordsLiDAR regression remote sensing soft computing
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- 3.Gonzalez-Ferreiro, E., Dieguez-Aranda, U., Gonçalves-Seco, L., Crecente, R., Miranda, D.: Estimation of biomass in eucalyptus globulus labill. forests using different LiDAR sampling densities. In: Proceedings of ForestSat (2010)Google Scholar
- 5.Osborne, J., Waters, E.: Four assumptions of multiple regression that researchers should always test. Practical Assessment, Research and Evaluation 8(2) (2002)Google Scholar
- 8.Chen, G., Hay, G.J.: A support vector regression approach to estimate forest biophysical parameters at the object level using airborne lidar transects and quickbird data. Photogrammetric Engineering and Remote Sensing 77(7), 733–741 (2011)Google Scholar
- 14.Dieguez-Aranda, U., et al.: Herramientas selvicolas para la gestion forestal sostenible en Galicia. Xunta de Galicia (2009)Google Scholar
- 15.McGaughey, R.: FUSION/LDV: Software for LIDAR Data Analysis and Visualization. US Department of Agriculture, Forest Service, Pacific Northwest Research Station, Seattle (2009)Google Scholar
- 16.Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explorations 11(1) (2009)Google Scholar
- 18.Parejo, J.A., García, J., Ruiz-Cortés, A., Riquelme, J.C.: Statservice: Herramienta de análisis estadístico como soporte para la investigación con metaheurísticas. In: Actas del VIII Congreso Expañol sobre Metaheurísticas, Algoritmos Evolutivos y Bio-inspirados (2012)Google Scholar