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A New Machine Learning Paradigm for Terrain Reconstruction

Publication Type : Journal Article

Publisher : IEEE Geoscience Remote Sensing Letters

Source : IEEE Geoscience Remote Sensing Letters

Campus : Coimbatore

School : School of Engineering

Year : 2006

Abstract : Terrain models that permit multiresolution access are essential for model predictive control of unmanned aerial vehicles in low-level flights. The authors present the extreme learning machine (ELM), a recently proposed learning paradigm, as a mechanism for learning the stored digital elevation information to allow multiresolution access. We give results of simulations designed to compare the performance of our approach with two other approaches for multiresolution access, namely: 1) linear interpolation on Delaunay triangles of the sampled terrain data points and 2) terrain learning using support vector machines (SVMs). The results show that to achieve the same mean square error during access, the memory needed in our approach is significantly lower. Additionally, the offline training time for the ELM network is much less than that for the SVM.

Cite this Research Publication : C.W.T. Yeu, Meng-Hiot Lim, G.B. Huang, Amit Agarwal, Yew-Soon Ong: A new machine learning paradigm for terrain reconstruction. IEEE Geoscience Remote Sensing Letters, vol. 3, no. 3, pp. 382-386 (2006)

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