Terrain Modeling Using Machine Learning Methods

• Published in: 2006 9th International Conference on Control, Automation, Robotics and Vision pp. 1 - 4
• Main Authors: Yeu, C.-W.T., Meng-Hiot Lim, Guang-Bin Huang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2006
• Subjects: Back Propagation; Computational efficiency; Computer networks; Delaunay Triangulation; ELM; Function approximation; Iterative methods; Learning systems; Machine learning; Navigation; Neural networks; Scattering; Surface reconstruction; Terrain Modeling
• ISBN: 1424403413; 9781424403417
• DOI: 10.1109/ICARCV.2006.345471

The problem of terrain modeling is basically a type of function approximation problem. This type of problem has been widely studied in the soft computing community. In recent years, neural networks have been successfully applied to surface reconstruction and classification problems involving scattered data. However, due to the iterative nature of training a neural network, the resulting high cost in computational time limits the implementation of machine learning based methods in many real world applications (for example, navigation applications in unmanned aerial vehicles) that require fast generation of terrain models. A recently proposed machine learning method, the extreme learning machine (ELM), is able to train single-layer feed forward neural networks with excellent speed and good generalization. In this paper, we present terrain modeling using various machine learning methods, and we compare the performances of these methods with ELM. We also present a comparison of terrain modeling performances between ELM and the popular choice of terrain and surface modeling technique, the Delaunay triangulation with linear interpolation. Our results show that machine learning using ELM offers a potential solution to terrain modeling problems with good performances