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

Foody, Giles M.

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
• 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