Machine Learning Applications for Earth Observation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2018

AUTHORS

David J. Lary , Gebreab K. Zewdie , Xun Liu , Daji Wu , Estelle Levetin , Rebecca J. Allee , Nabin Malakar , Annette Walker , Hamse Mussa , Antonio Mannino , Dirk Aurin

ABSTRACT

Machine learning has found many applications in remote sensing. These applications range from retrieval algorithms to bias correction, from code acceleration to detection of disease in crops, from classification of pelagic habitats to rock type classification. As a broad subfield of artificial intelligence, machine learning is concerned with algorithms and techniques that allow computers to “learn” by example. The major focus of machine learning is to extract information from data automatically by computational and statistical methods. Over the last decade there has been considerable progress in developing a machine learning methodology for a variety of Earth Science applications involving trace gases, retrievals, aerosol products, land surface products, vegetation indices, and most recently, ocean applications. In this chapter, we will review some examples of how machine learning has already been useful for remote sensing and some likely future applications. More... »

PAGES

165-218

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  • Book

    TITLE

    Earth Observation Open Science and Innovation

    ISBN

    978-3-319-65632-8
    978-3-319-65633-5

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-65633-5_8

    DOI

    http://dx.doi.org/10.1007/978-3-319-65633-5_8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1100588547


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