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Scientifica
Volume 2017, Article ID 9806479, 8 pages
https://doi.org/10.1155/2017/9806479
Research Article

A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data

1Department of Informatics, Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba 265-8501, Japan
2Graduate School of Informatics, Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba 265-8501, Japan

Correspondence should be addressed to Ram C. Sharma; moc.liamg@10amrahscmar

Received 4 January 2017; Revised 9 April 2017; Accepted 18 April 2017; Published 11 June 2017

Academic Editor: Dick de Ridder

Copyright © 2017 Ram C. Sharma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to Cite this Article

Ram C. Sharma, Keitarou Hara, and Hidetake Hirayama, “A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data,” Scientifica, vol. 2017, Article ID 9806479, 8 pages, 2017. https://doi.org/10.1155/2017/9806479.