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 cover classifications from airborne thematic mapper data are explored. Variations in sampling intensity and effort are highlighted in a dataset that is widely used in mapping and modelling studies; these may need accounting for in analyses. The quality of the labelling in the reference dataset was also a key variable influencing mapping accuracy. Accuracy varied with the amount and nature of mislabelled training cases with the nature of the effects varying between classifiers. The largest impacts on accuracy occurred when mislabelling involved confusion between similar classes. Accuracy was also typically negatively related to the magnitude of mislabelled cases and the support vector machine (SVM), which has been claimed to be relatively insensitive to training data error, was the most sensitive of the set of classifiers investigated, with overall classification accuracy declining by 8% (significant at 95% level of confidence) with the use of a training set containing 20% mislabelled cases.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Engineering Systems and Supply Chain Management
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College of Engineering & Physical Sciences > Sustainable environment research group
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences
Additional Information: 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. (CC BY 4.0).
Uncontrolled Keywords: accuracy,classification,error,land cover,remote sensing,training,Geography, Planning and Development,Computers in Earth Sciences,Earth and Planetary Sciences (miscellaneous)
Publication ISSN: 2220-9964
Last Modified: 15 May 2024 07:12
Date Deposited: 28 Nov 2016 15:35
Full Text Link: http://www.mdpi ... 0-9964/5/11/199
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2016-11-01
Accepted Date: 2016-10-23
Submitted Date: 2016-08-23
Authors: Foody, Giles M.
Pal, Mahesh
Rocchini, Duccio
Garzon-Lopez, Carol X.
Bastin, Lucy (ORCID Profile 0000-0003-1321-0800)



Version: Published Version

License: Creative Commons Attribution

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