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A new machine learning paradigm for terrain reconstruction

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A new machine learning paradigm for terrain reconstruction

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dc.contributor.author Yeu, Thomas Chee Wee
dc.contributor.author Lim, Meng-Hiot
dc.contributor.author Huang, Guang Bin
dc.contributor.author Agarwal, Amit
dc.contributor.author Ong, Yew Soon
dc.date.accessioned 2010-08-17T05:48:29Z
dc.date.available 2010-08-17T05:48:29Z
dc.date.copyright 2006
dc.date.issued 2010-08-17T05:48:29Z
dc.identifier.citation Yeu, C. W., Lim, M. H., Huang, G. B., Agarwal, A., & Ong, Y. S. (2006). A new machine learning paradigm for terrain reconstruction. Geoscience and Remote Sensing Letters. 3(3), 382-386.
dc.identifier.issn 1545-598X
dc.identifier.uri http://hdl.handle.net/10220/6308
dc.description.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.
dc.format.extent 5 p.
dc.language.iso en
dc.relation.ispartofseries Geoscience and remote sensing letters
dc.rights © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. http://www.ieee.org/portal/site This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
dc.subject DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering.
dc.title A new machine learning paradigm for terrain reconstruction
dc.type Journal Article
dc.contributor.school School of Electrical and Electronic Engineering
dc.identifier.doi http://dx.doi.org/10.1109/LGRS.2006.873687
dc.description.version Published version

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