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The Sensitivity of Mapping Methods to Reference Data Quality: Training Supervised Image Classifications with Imperfect Reference Data

School of Geography, University of Nottingham, Nottingham, NG7 2RD, UK
Department of Civil Engineering, National Institute of Technology, Kurukshetra, Haryana 136119, India
Department of Biodiversity and Molecular Ecology, Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, 38010 San Michele all’dige TN, Italy
Ecology and Dynamics of Human-Influenced Systems Research Unit (EDYSAN, FRE 3498 CNRS), University of Picardy Jules Verne, 1 rue des Louvels, FR-80037 Amiens Cedex 1, France
School of Engineering and Applied Science, Aston University, Birmingham, B4 7ET, UK
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(11), 199;
Received: 23 August 2016 / Revised: 8 October 2016 / Accepted: 23 October 2016 / Published: 1 November 2016
PDF [936 KB, uploaded 1 November 2016]


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. View Full-Text
Keywords: classification; training; error; accuracy; remote sensing; land cover classification; training; error; accuracy; remote sensing; land cover

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Foody, G.M.; Pal, M.; Rocchini, D.; Garzon-Lopez, C.X.; Bastin, L. The Sensitivity of Mapping Methods to Reference Data Quality: Training Supervised Image Classifications with Imperfect Reference Data. ISPRS Int. J. Geo-Inf. 2016, 5, 199.

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