Value of dimensionality reduction for crop differentiation with multi-temporal imagery and machine learning

Value of dimensionality reduction for crop differentiation with multi-temporal imagery and... •A diverse set of features dramatically increases crop classification accuracy.•SVM is not greatly affected by high-feature dimensionality for crop differentiation.•The SVM algorithm can be applied to the full set of generated features.•Principal Components Analysis holds much potential for crop-type classification. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computers and Electronics in Agriculture Elsevier

Value of dimensionality reduction for crop differentiation with multi-temporal imagery and machine learning

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier B.V.
ISSN
0168-1699
eISSN
1872-7107
D.O.I.
10.1016/j.compag.2017.08.024
Publisher site
See Article on Publisher Site

Abstract

•A diverse set of features dramatically increases crop classification accuracy.•SVM is not greatly affected by high-feature dimensionality for crop differentiation.•The SVM algorithm can be applied to the full set of generated features.•Principal Components Analysis holds much potential for crop-type classification.

Journal

Computers and Electronics in AgricultureElsevier

Published: Nov 1, 2017

References

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