Automatic arable land detection with supervised machine learning View Full Text


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Article Info

DATE

2016-11

AUTHORS

R. B. Arango, I. Díaz, A. Campos, E. R. Canas, E. F. Combarro

ABSTRACT

In Precision Agriculture one of the basic tasks is the classification of land zones in either arable or non-arable land. Several studies have been conducted using data obtained from soil analysis or local exploration of the parcels. However, sometimes only data from satellite images are available and then the problem not only becomes more challenging but also more interesting to solve because it is much more cost-effective. In this paper, we consider different spectral and thermal bands from the Landsat 8 satellite images corresponding to a vineyard located in Galicia, a region in Northeastern Spain, and apply a range of supervised Machine Learning methods to classify the different land zones. We conclude that an adequate choice of the algorithm parameters together with feature selection techniques can yield a classification that is both highly effective and efficient. More... »

PAGES

535-545

References to SciGraph publications

  • 1995-09. Support-vector networks in MACHINE LEARNING
  • 2013. Applied Predictive Modeling in NONE
  • Journal

    TITLE

    Earth Science Informatics

    ISSUE

    4

    VOLUME

    9

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s12145-016-0270-6

    DOI

    http://dx.doi.org/10.1007/s12145-016-0270-6

    DIMENSIONS

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