| Autor: |
Giles M. Foody |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Remote Sensing, Vol 14, Iss 21, p 5380 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2072-4292 |
| DOI: |
10.3390/rs14215380 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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