Supervised methods of image segmentation accuracy assessment in land cover mapping

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

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.

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

• Journal Article Type: Article
• Acceptance Date: Nov 29, 2017
• Online Publication Date: Dec 11, 2017
• Publication Date: Feb 1, 2018
• Deposit Date: Jan 8, 2018
• Publicly Available Date: Dec 12, 2018
• Journal: Remote Sensing of Environment
• Print ISSN: 0034-4257
• Electronic ISSN: 0034-4257
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 205
• DOI: https://doi.org/10.1016/j.rse.2017.11.024
• Keywords: OBIA; GEOBIA; Empirical goodness methods; Quality; Classification
• Public URL: https://nottingham-repository.worktribe.com/output/908914
• Publisher URL: https://doi.org/10.1016/j.rse.2017.11.024