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Supervised methods of image segmentation accuracy assessment in land cover mapping

Costa, Hugo; Foody, Giles M.; Boyd, Doreen S.

Authors

Hugo Costa

GILES FOODY giles.foody@nottingham.ac.uk
Professor of Geographical Information

DOREEN BOYD doreen.boyd@nottingham.ac.uk
Professor of Earth Observation



Abstract

Land cover mapping via image classification is sometimes realized through object-based image analysis. Objects are typically constructed by partitioning imagery into spatially contiguous groups of pixels through image segmentation and used as the basic spatial unit of analysis. As it is typically desirable to know the accuracy with which the objects have been delimited prior to undertaking the classification, numerous methods have been used for accuracy assessment. This paper reviews the state-of-the-art of image segmentation accuracy assessment in land cover mapping applications. First the literature published in three major remote sensing journals during 2014–2015 is reviewed to provide an overview of the field. This revealed that qualitative assessment based on visual interpretation was a widely-used method, but a range of quantitative approaches is available. In particular, the empirical discrepancy or supervised methods that use reference data for assessment are thoroughly reviewed as they were the most frequently used approach in the literature surveyed. Supervised methods are grouped into two main categories, geometric and non-geometric, and are translated here to a common notation which enables them to be coherently and unambiguously described. Some key considerations on method selection for land cover mapping applications are provided, and some research needs are discussed.

Journal Article Type Article
Publication Date Feb 1, 2018
Journal Remote Sensing of Environment
Print ISSN 0034-4257
Electronic ISSN 0034-4257
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 205
APA6 Citation Costa, H., Foody, G. M., & Boyd, D. S. (2018). Supervised methods of image segmentation accuracy assessment in land cover mapping. Remote Sensing of Environment, 205, https://doi.org/10.1016/j.rse.2017.11.024
DOI https://doi.org/10.1016/j.rse.2017.11.024
Keywords OBIA; GEOBIA; Empirical goodness methods; Quality; Classification
Publisher URL https://doi.org/10.1016/j.rse.2017.11.024
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0





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