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Cited by 248Remote-Sensing Image ClassificationRemote Sensing and Land UseStatistical Methods in Epidemiology

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

Foody, Giles M.·2009·International Journal of Remote Sensing
DOI 10.1080/01431160903130937
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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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Crossref · 10.1080/01431160903130937

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73 of 73 references verified0 citation errors · 0 not checkable

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Citations

Cites

Show all 74 references

Research works printed but not linked: 7

  1. Aberson C., 2002, Journal of Articles in Support of the Null Hypothesis, 1, 36
  2. Cochran W. G., 1977, Sampling Techniques
  3. Congalton R. G., 1983, Photogrammetric Engineering and Remote Sensing, 49, 1671
  4. Hay A. M., 1979, Photogrammetric Engineering and Remote Sensing, 45, 529
  5. Prisley S. P., 1987, Photogrammetric Engineering and Remote Sensing, 53, 1259
  6. Rosenfield G. H., 1986, Photogrammetric Engineering and Remote Sensing, 52, 223
  7. Saeed N., 2006, European Journal of Scientific Research, 14, 319

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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
DOI
10.1080/01431160903130937
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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