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Global and Local Assessment of Image Classification Quality on an Overall and Per-Class Basis without Ground Reference Data

Foody, Giles M.

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Authors

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



Abstract

Ground reference data are typically required to evaluate the quality of a supervised image classification analysis used to produce a thematic map from remotely sensed data. Acquiring a suitable ground data set for a rigorous assessment of classification quality can be a major challenge. An alternative approach to quality assessment is to use a model-based method such as can be achieved with a latent class analysis. Previous research has shown that the latter can provide estimates of class areal extent for a non-site specific accuracy assessment and yield estimates of producer’s accuracy which are commonly used in site-specific accuracy assessment. Here, the potential for quality assessment via a latent class analysis is extended to show that an estimate of a complete confusion matrix can be predicted which allows a suite of standard accuracy measures to be generated to indicate global quality on an overall and per-class basis. In addition, information on classification uncertainty may be used to illustrate classification quality on a per-pixel basis and hence provide local information to highlight spatial variations in classification quality. Classifications of imagery from airborne and satellite-borne sensors were used to illustrate the potential of the latent class analysis with results compared against those arising from the use of a conventional ground data set.

Citation

Foody, G. M. (2022). Global and Local Assessment of Image Classification Quality on an Overall and Per-Class Basis without Ground Reference Data. Remote Sensing, 14(21), Article 5380. https://doi.org/10.3390/rs14215380

Journal Article Type Article
Acceptance Date Oct 21, 2022
Online Publication Date Oct 27, 2022
Publication Date Nov 1, 2022
Deposit Date Oct 28, 2022
Publicly Available Date Oct 28, 2022
Journal Remote Sensing
Electronic ISSN 2072-4292
Publisher MDPI AG
Peer Reviewed Peer Reviewed
Volume 14
Issue 21
Article Number 5380
DOI https://doi.org/10.3390/rs14215380
Keywords General Earth and Planetary Sciences
Public URL https://nottingham-repository.worktribe.com/output/12900608
Publisher URL https://www.mdpi.com/2072-4292/14/21/5380

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