The sensitivity of mapping methods to reference data quality: training supervised image classifications with imperfect reference data

The sensitivity of mapping methods to reference data quality: training supervised image classifications with imperfect reference data

The accuracy of a map is dependent on the reference dataset used in its construction. Classification analyses used in thematic mapping can, for example, be sensitive to a range of sampling and data quality concerns. With particular focus on the latter, the effects of reference data quality on land c...

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Bibliographic Details
Main Authors: Foody, Giles M., Pal, Mahesh, Rocchini, Duccio, Garzon-Lopez, Carol X., Bastin, Lucy
Format: Article
Published: MDPI 2016
Subjects:
classification; training; error; accuracy; remote sensing; land cover
Online Access:https://eprints.nottingham.ac.uk/38443/

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