The DOI matches its Crossref record; 73 of the 74 references it cites were found in a registry or an index.
Sample size determination for image classification accuracy assessment and comparison
Abstract
Many factors influence the quality and value of a classification accuracy assessment and evaluation programme. This paper focuses on the size of the testing set(s) used with particular regard to the impacts on accuracy assessment and comparison. Testing set size is important as the use of an inappropriately large or small sample could lead to limited and sometimes erroneous assessments of accuracy and of differences in accuracy. Here, some of the basic statistical principles of sample size determination are outlined, including a discussion of Type II errors and their control. The paper provides a discussion on some of the basic issues of sample size determination for accuracy assessment and includes factors linked to accuracy comparison. With the latter, the researcher should specify the effect size (minimum meaningful difference in accuracy), significance level and power used in an analysis and ideally also fit confidence limits to derived estimates. This will help design a study and aid the use of appropriate sample sizes, as well as facilitate interpretation of results. In particular, it will help avoid problems, such as under-powered analyses, and provide a richer information base for classification evaluation. The paper includes equations that could be used to determine sample sizes for common applications in remote sensing, using both independent and related samples.
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In the citing paper
“Hence, the neural network method critically depends on relatively larger training and test datasets compared to image processing methods [52-54].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [54]
In the citing paper
“However, they require significant amounts of data to accurately perform a classification, especially with increasing number of classes [52-54].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [54]
Citations
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Foody, G. M. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(01)00295-4 · Cited by 4,655
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Foody, G. M. (2010) · The Photogrammetric Record · doi:10.1111/j.1477-9730.2010.00574_2.x · Cited by 2,836
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Cited by
Sorted by citations
An assessment of the effectiveness of a random forest classifier for land-cover classification
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J. S., Chica‐Olmo, M., & Rigol-Sánchez, J. P. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2011.11.002 · Cited by 3,068
Zhang, X., Liu, L., Chen, X., Gao, Y., Xie, S., & Mi, J. (2021) · Earth system science data · doi:10.5194/essd-13-2753-2021 · Cited by 1,136
The global distribution and trajectory of tidal flats
Murray, N., Phinn, S., DeWitt, M., Ferrari, R., Johnston, R., Lyons, M., Clinton, N., Thau, D., & Fuller, R. A. (2018) · Nature · doi:10.1038/s41586-018-0805-8 · Cited by 1,120
Duro, D. C., Franklin, S. E., & Dubé, M. G. (2011) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.11.020 · Cited by 1,020
Key issues in rigorous accuracy assessment of land cover products
Stehman, S. V., & Foody, G. M. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.05.018 · Cited by 727
Metadata
- Authors
- Foody, G. M.
- Affiliations
- University of Nottingham (GB)
- Corresponding author
- Foody, Giles M.
- ORCID
- Foody, Giles M.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2009-09-23
- Volume
- 30
- Issue
- 20
- Pages
- 5273-5291
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2004206930
- Cited by
- 248
- References
- 74
- Citations by year
- 2026: 16 · 2025: 15 · 2024: 16 · 2023: 26 · 2022: 27 · 2021: 20 · 2020: 15 · 2019: 18 · 2018: 16 · 2017: 6 · 2016: 13 · 2015: 15 · 2014: 15 · 2013: 7 · 2012: 10
- FWCI
- 17.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Remote Sensing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Social Sciences › Geography, Planning and Development › Remote Sensing and Land Use
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- sample size determination, classification accuracy assessment, accuracy comparison, type II error, statistical power, effect size, confidence limits, remote sensing image classification
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