Sample size determination for image classification accuracy assessment and comparison

Foody, Giles M.

doi:10.1080/01431160903130937

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

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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The neural network method critically depends on relatively larger training and test datasets compared to image processing methods.
“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]
Neural networks require significant amounts of data to accurately perform a classification, especially with increasing number of classes.
“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]

Statistical Methods for Rates and Proportions
Fleiss, J. L., Levin, B., & Paik, M. C. (2003) · Wiley Series in Probability and Statistics · doi:10.1002/0471445428 · Cited by 18,646 · see citing works
Photogrammetric Engineering and Remote Sensing
Nicol, J., King, B., Quattrochi, D., Dowman, I. J., Ehlers, M., & Xiaoli, D. (2007) · UCL Discovery (University College London) · Cited by 9,325 · see citing works
Status of land cover classification accuracy assessment
Foody, G. M. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(01)00295-4 · Cited by 4,655 · see citing works
Assessing the Accuracy of Remotely Sensed Data
Congalton, R. G., & Green, K. (1998) · Mapping sciences series/Mapping science · doi:10.1201/9781420048568 · Cited by 3,434 · see citing works
Assessing the Accuracy of Remotely Sensed Data: Principles and Practices
Foody, G. M. (2010) · The Photogrammetric Record · doi:10.1111/j.1477-9730.2010.00574_2.x · Cited by 2,836 · see citing works
American Journal of Neuroradiology
(2020) · American Journal of Neuroradiology · doi:10.3174/ajnr · Cited by 2,468 · see citing works
The Abuse of Power: The Pervasive Fallacy of Power Calculations for Data Analysis
Hoenig, J. M., & Heisey, D. M. (2001) · The American Statistician · doi:10.1198/000313001300339897 · Cited by 2,046 · see citing works
Statistics notes: Absence of evidence is not evidence of absence
Altman, D. G., & Bland, J. M. (1995) · BMJ · doi:10.1136/bmj.311.7003.485 · Cited by 1,946 · see citing works
Selecting and interpreting measures of thematic classification accuracy
Stehman, S. V. (1997) · Remote Sensing of Environment · doi:10.1016/s0034-4257(97)00083-7 · Cited by 1,745 · see citing works
Thematic Map Comparison
Foody, G. M. (2004) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.70.5.627 · Cited by 1,065 · see citing works
A coefficient of agreement as a measure of thematic classification accuracy.
Rosenfield, G. H., & Fitzpatrick-Lins, K. (1986) · Photogrammetric Engineering & Remote Sensing · Cited by 936 · see citing works
Classification and feature extraction for remote sensing images from urban areas based on morphological transformations
Benediktsson, J. A., Pesaresi, M., & Arnason, K. (2003) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2003.814625 · Cited by 762 · see citing works
Sample Size Determination
Dell, R. B., Holleran, S., & Ramakrishnan, R. (2002) · ILAR Journal · doi:10.1093/ilar.43.4.207 · Cited by 758 · see citing works
Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques.
Congalton, R. G., Oderwald, R. G., & Mead, R. A. (1983) · University of New Hampshire Scholars Repository (University of New Hampshire at Manchester) · Cited by 680 · see citing works
PCA‐based land‐use change detection and analysis using multitemporal and multisensor satellite data
Deng, J., Wang, K., Deng, Y., & Qi, G. J. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160801950162 · Cited by 659 · see citing works
The Use of Predicted Confidence Intervals When Planning Experiments and the Misuse of Power When Interpreting Results
Goodman, S. N., & Berlin, J. A. (1994) · Annals of Internal Medicine · doi:10.7326/0003-4819-121-3-199408010-00008 · Cited by 658 · see citing works
Comparative assessment of the measures of thematic classification accuracy
Liu, C., Frazier, P. S., & Kumar, L. (2006) · Remote Sensing of Environment · doi:10.1016/j.rse.2006.10.010 · Cited by 465 · see citing works
Retrospective Power Analysis
Thomas, L. (1997) · Conservation Biology · doi:10.1046/j.1523-1739.1997.96102.x · Cited by 410 · see citing works
A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas
Shackelford, A. K., & Davis, C. H. (2003) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2003.815972 · Cited by 368 · see citing works
Harshness in image classification accuracy assessment
Foody, G. M. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701442120 · Cited by 358 · see citing works
Multispectral landuse classification using neural networks and support vector machines: one or the other, or both?
Dixon, B., & Candade, N. (2007) · International Journal of Remote Sensing · doi:10.1080/01431160701294661 · Cited by 308 · see citing works
Meta-analysis of multiple outcomes by regression with random effects
Berkey, C. S., Hoaglin, D. C., Antczak‐Bouckoms, A., Mosteller, F. C., & Colditz, G. A. (1998) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19981130)17:22 · Cited by 274 · see citing works
Sub‐pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super‐resolution pixel‐swapping
Thornton, M. W., Atkinson, P. M., & Holland, D. A. (2006) · International Journal of Remote Sensing · doi:10.1080/01431160500207088 · Cited by 263 · see citing works
Confidence intervals are a more useful complement to nonsignificant tests than are power calculations
Colegrave, N. (2003) · Behavioral Ecology · doi:10.1093/beheco/14.3.446 · Cited by 251 · see citing works
Classification accuracy comparison: Hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority
Foody, G. M. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.03.014 · Cited by 232 · see citing works
Land cover classification in the Argentine Pampas using multi-temporal Landsat TM data
Guerschman, J. P., Paruelo, J. M., Bella, C. M. D., Giallorenzi, M. C., & Pacín, F. (2003) · International Journal of Remote Sensing · doi:10.1080/0143116021000021288 · Cited by 223 · see citing works
Sample Survey: Principles and Methods.
RS, & Barnett, V. (1995) · Journal of the American Statistical Association · doi:10.2307/2291109 · Cited by 209 · see citing works
Sample Size for Testing Differences in Proportions for the Paired-Sample Design
Connor, R. (1987) · Biometrics · doi:10.2307/2531961 · Cited by 189 · see citing works
Comparing accuracy assessments to infer superiority of image classification methods
de Leeuw, J., Jia, H., Yang, L. X., Liu, X., Schmidt, K. S., & Skidmore, A. K. (2006) · International Journal of Remote Sensing · doi:10.1080/01431160500275762 · Cited by 166 · see citing works
Basic probability sampling designs for thematic map accuracy assessment
Stehman, S. V. (1999) · International Journal of Remote Sensing · doi:10.1080/014311699212100 · Cited by 162 · see citing works
No Significant Difference?
Ewell, P. T. (1999) · Change The Magazine of Higher Learning · doi:10.1080/00091389909602682 · Cited by 141 · see citing works
The Matched Pairs Design in the Case of All-or-None Responses
Miettinen, O. S. (1968) · Biometrics · doi:10.2307/2528039 · Cited by 133 · see citing works
Sampling designs to test land-use map accuracy
HAY, A. M. (1979) · Cited by 128 · see citing works
An accuracy assessment framework for large‐area land cover classification products derived from medium‐resolution satellite data
Wulder, M. A., Franklin, S. E., White, J. C., Linke, J., & Magnussen, S. (2005) · International Journal of Remote Sensing · doi:10.1080/01431160500185284 · Cited by 118 · see citing works
Sample Survey Principles and Methods.
Skinner, C. J., & Barnett, V. (1992) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2982900 · Cited by 113 · see citing works
A confidence interval approach to data analysis
Stefano, J. D. (2003) · Forest Ecology and Management · doi:10.1016/s0378-1127(03)00331-1 · Cited by 110 · see citing works
Determination of sample size.
Naing, N. N. (2003) · PubMed · Cited by 106 · see citing works
RVM‐based multi‐class classification of remotely sensed data
Foody, G. M. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701822115 · Cited by 84 · see citing works
Practical Sampling Techniques
Som, R. K. (1995) · CRC Press eBooks · doi:10.1201/9781482273465 · Cited by 83 · see citing works
Expert system classification of urban land use/cover for Delhi, India
Wentz, E. A., Nelson, D., Rahman, A., Stefanov, W. L., & Roy, S. S. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160801905497 · Cited by 77 · see citing works
Interpreting Null Results: Improving Presentation and Conclusions with Confidence Intervals 1
Aberson, C. L. (2002) · Cited by 74 · see citing works
A TREE-BASED METHOD OF ANALYSIS FOR PROSPECTIVE STUDIES
Heping Zhang, D., Holford, T. R., & Bracken, M. B. (1996) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19960115)15:1 · Cited by 62 · see citing works
Multisource Classification Using Support Vector Machines
Watanachaturaporn, P., Arora, M. K., & Varshney, P. K. (2008) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.74.2.239 · Cited by 59 · see citing works
SVM‐based segmentation and classification of remotely sensed data
Lizarazo, I. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160802326081 · Cited by 54 · see citing works
An improved SVM method P‐SVM for classification of remotely sensed data
Zhang, R., & Ma, J. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160802220151 · Cited by 54 · see citing works
Using a hybrid fuzzy classifier (HFC) to map typical grassland vegetation in Xilin River Basin, Inner Mongolia, China
Sha, Z., Bai, Y., Xie, Y., Yu, M., & Zhang, L. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701408436 · Cited by 52 · see citing works
Sample size
Carlin, J., & Doyle, L. (2002) · Journal of Paediatrics and Child Health · doi:10.1046/j.1440-1754.2002.00855.x · Cited by 51 · see citing works
Mapping an inland wetland complex using hyperspectral imagery
Jollineau, M., & Howarth, P. J. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701469099 · Cited by 49 · see citing works
A multiscale feature fusion approach for classification of very high resolution satellite imagery based on wavelet transform
Huang, X., Zhang, L., & Li, P. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160802139922 · Cited by 48 · see citing works
Evaluation of global land cover data sets over the tundra–taiga transition zone in northernmost Finland
Heiskanen, J. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701871104 · Cited by 46 · see citing works
Assessing the impact of the 2004 tsunami on mangroves using remote sensing and GIS techniques
Sirikulchayanon, P., Sun, W., & Oyana, T. J. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701646332 · Cited by 46 · see citing works
Accuracy Assessment
Stehman, S. V., & Foody, G. M. (2013) · Sage eBooks · doi:10.4135/9780857021052.n21 · Cited by 44 · see citing works
Long‐term monitoring of land cover changes based on Landsat imagery to improve hydrological modelling in West Africa
Ruelland, D., Dezetter, A., Puech, C., & Ardoin‐Bardin, S. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701758699 · Cited by 43 · see citing works
Using classification error matrices to improve the accuracy of weighted land-cover models
Prisley, S. P., & Smith, J. L. (1987) · Cited by 42 · see citing works
Technical Note. Mapping seagrass beds using IKONOS satellite image and side scan sonar measurements: a Japanese case study
Sagawa, T., Mikami, A., Komatsu, T., Kosaka, N., Kosako, A., Miyazaki, S., & Takahashi, M. (2007) · International Journal of Remote Sensing · doi:10.1080/01431160701269028 · Cited by 40 · see citing works
Are there any differences? A non-sensical question in ecology
Martínez‐Abraín, A. (2007) · Acta Oecologica · doi:10.1016/j.actao.2007.04.003 · Cited by 37 · see citing works
Evaluation of segment‐based gap‐filled Landsat ETM+ SLC‐off satellite data for land cover classification in southern Saskatchewan, Canada
Bédard, F., Reichert, G., Dobbins, R., & Trépanier, I. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701281064 · Cited by 37 · see citing works
Confidence intervals and sample sizes: don't throw out all your old sample size tables.
Daly, L. (1991) · BMJ · doi:10.1136/bmj.302.6772.333 · Cited by 36 · see citing works
Test Statistic and Sample Size for a Two-Sample McNemar Test
Feuer, E. J., & Kessler, L. G. (1989) · Biometrics · doi:10.2307/2531505 · Cited by 36 · see citing works
Near‐infrared discrimination of leafless saltcedar in wintertime Landsat TM
Groeneveld, D. P., & Watson, R. P. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701711078 · Cited by 34 · see citing works
Phase correlation based redundancy removal in feature weighting band selection for hyperspectral images
Demir, B., & Ertürk, Ş. M. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701802471 · Cited by 30 · see citing works
Statistics for clinicians: 7: Sample size
Carlin, J. B., & Doyle, L. W. (2002) · Journal of Paediatrics and Child Health · Cited by 28 · see citing works
Confidence Intervals from the Difference between Two Correlated Proportions
Lloyd, C. J. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10474989 · Cited by 27 · see citing works
Land use studies in drylands: an evaluation of object‐oriented classification of very high resolution panchromatic imagery
Elmqvist, B., Ardö, J., & Olsson, L. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160802238419 · Cited by 24 · see citing works
Design, analysis, and inference for studies comparing thematic accuracy of classified remotely sensed data: a special case of map comparison
Stehman, S. V. (2006) · Journal of Geographical Systems · doi:10.1007/s10109-006-0022-0 · Cited by 19 · see citing works
Artificial immune‐based supervised classifier for land‐cover classification
Pal, M. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701408402 · Cited by 18 · see citing works
Bayesian Multi‐net Classifier for classification of remote sensing data
Ouyang, Y., Ma, J., & Dai, Q. (2006) · International Journal of Remote Sensing · doi:10.1080/01431160600794605 · Cited by 16 · see citing works
Landscape Complexity and Remote Classification in Eastern Coastal Mexico: Applications of Landsat‐7 ETM+Data
Crews‐Meyer, K. A., Hudson, P. F., & Colditz, R. R. (2004) · Geocarto International · doi:10.1080/10106040408542298 · Cited by 15 · see citing works
No significant difference ... Says who?
Trout, A. T., Kaufmann, T. J., & Kallmes, D. F. (2007) · PubMed · Cited by 14 · see citing works
Statistics for Clinicians
Bahna, S. L. (2009) · Annals of Allergy Asthma & Immunology · doi:10.1016/s1081-1206(10)60812-5 · Cited by 13 · see citing works
Sample-size needs for forestry herbicide trials
Zedaker, S. M., Grégoire, T. G., & Miller, J. H. (1993) · Canadian Journal of Forest Research · doi:10.1139/x93-268 · Cited by 12 · see citing works
Some Practical Considerations and a Crude Formula for Estimating Sample Size for McNemar’s Test
Lehr, R. G. (2001) · Drug Information Journal · doi:10.1177/009286150103500419 · Cited by 7 · see citing works
Untitled work
Buyse, M. (1999) · doi:10.1002/(sici)1097-0258(19991230)18:24

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Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F., & Wang, S. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.02.055 · Cited by 611 · see citing works
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GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method
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From land cover-graphs to urban structure types
Walde, I., Hese, S., Berger, C. R., & Schmullius, C. (2014) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2013.865189 · Cited by 72 · see citing works
Trends of land surface phenology derived from passive microwave and optical remote sensing systems and associated drivers across the dry tropics 1992–2012
Tong, X., Tian, F., Brandt, M., Liu, Y., Zhang, W., & Fensholt, R. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111307 · Cited by 70 · see citing works
Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection
Aust, J., Shankland, S., Pons, D., Mukundan, R., & Mitrovic, A. (2021) · Aerospace · doi:10.3390/aerospace8020030 · Cited by 66 · see citing works
Using Volunteered Data in Land Cover Map Validation: Mapping West African Forests
Foody, G. M., & Boyd, D. S. (2013) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2013.2250257 · Cited by 66 · see citing works
Comparison of Masking Algorithms for Sentinel-2 Imagery
Zekoll, V., Main-Knorn, M., Alonso, K., Louis, J., Frantz, D., Richter, R., & Pflug, B. M. (2021) · Remote Sensing · doi:10.3390/rs13010137 · Cited by 70 · see citing works
Mangrove species mapping in coastal China using synthesized Sentinel-2 high-separability images
Zhao, C., Jia, M., Zhang, R., Wang, Z., Ren, C., Mao, D., & Wang, Y. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2024.114151 · Cited by 65 · see citing works
Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain
Grebby, S. R., Naden, J., Cunningham, D., & Tansey, K. J. (2010) · Remote Sensing of Environment · doi:10.1016/j.rse.2010.08.019 · Cited by 64 · see citing works
Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data
Dubeau, P., King, D. J., Unbushe, D., & Rebelo, L. (2017) · Remote Sensing · doi:10.3390/rs9101056 · Cited by 63 · see citing works
Land Use and Land Cover Change Dynamics across the Brazilian Amazon: Insights from Extensive Time-Series Analysis of Remote Sensing Data
Carreiras, J. M. B., Jones, J., Lucas, R., & Gabriel, C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104144 · Cited by 62 · see citing works
Damage assessment on concrete surfaces using multi-spectral image analysis
Valença, J., Gonçalves, L. M. S., & Júlio, E. (2012) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2012.11.061 · Cited by 59 · see citing works
Evaluation of ALOS PALSAR sensitivity for characterizing natural forest cover in wider tropical areas
Thapa, R. B., Itoh, T., Shimada, M., Watanabe, M., Motohka, T., & Shiraishi, T. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.04.025 · Cited by 55 · see citing works
Mapping 10-m Resolution Rural Settlements Using Multi-Source Remote Sensing Datasets with the Google Earth Engine Platform
Ji, H., Li, X., Wei, X., Liu, W., Zhang, L., & Wang, L. (2020) · Remote Sensing · doi:10.3390/rs12172832 · Cited by 54 · see citing works
GLC_FCS10: a global 10 m land-cover dataset with a fine classification system from Sentinel-1 and Sentinel-2 time-series data in Google Earth Engine
Zhang, X., Liu, L., Zhao, T., Zhang, W., Guan, L., Bai, M., & Chen, X. (2025) · Earth system science data · doi:10.5194/essd-17-4039-2025 · Cited by 53 · see citing works
A Web Platform Development to Perform Thematic Accuracy Assessment of Sugarcane Mapping in South-Central Brazil
Adami, M., Mello, M. P., Aguiar, D. A., Rudorff, B. F. T., & de Souza, A. F. (2012) · Remote Sensing · doi:10.3390/rs4103201 · Cited by 53 · see citing works
A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery
Su, Y., Guo, Q., Fry, D. L., Collins, B. M., Kelly, M., Flanagan, J., & Battles, J. J. (2015) · Canadian Journal of Remote Sensing · doi:10.1080/07038992.2016.1131114 · Cited by 52 · see citing works
Spatiotemporal Monitoring of Urban Sprawl in a Coastal City Using GIS-Based Markov Chain and Artificial Neural Network (ANN)
Mansour, S., Ghoneim, E. M., El-Kersh, A., Said, S., & Abdelnaby, S. (2023) · Remote Sensing · doi:10.3390/rs15030601 · Cited by 51 · see citing works
Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data
Lu, L., Chen, Y., Hawbaker, T. J., Zhu, Z., & Gong, P. (2014) · Remote Sensing · doi:10.3390/rs6065696 · Cited by 50 · see citing works
A landscape shape index-based sampling approach for land cover accuracy assessment
Chen, F., Chen, J., Wu, H., Hou, D., Zhang, W., Zhang, J., Zhou, X., & Chen, L. (2016) · Science China Earth Sciences · doi:10.1007/s11430-015-5280-5 · Cited by 51 · see citing works
Predicting species diversity in agricultural environments using Landsat TM imagery
Duro, D. C., Girard, J., King, D. J., Fahrig, L., Mitchell, S. W., Lindsay, K. E. F., & Tischendorf, L. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.01.001 · Cited by 53 · see citing works
Impacts of imperfect reference data on the apparent accuracy of species presence-absence models and their predictions
Foody, G. M. (2010) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2010.00605.x · Cited by 48 · see citing works
Assessing the Repeatability of Automated Seafloor Classification Algorithms, with Application in Marine Protected Area Monitoring
Leon, A. Z., Huvenne, V. A. I., Benoist, N. M. A., Ferguson, M., Bett, B. J., & Wynn, R. B. (2020) · Remote Sensing · doi:10.3390/rs12101572 · Cited by 48 · see citing works
Transition index maps for urban growth simulation: application of artificial neural networks, weight of evidence and fuzzy multi-criteria evaluation
Shafizadeh‐Moghadam, H., Tayyebi, A., & Helbich, M. (2017) · Environmental Monitoring and Assessment · doi:10.1007/s10661-017-5986-3 · Cited by 46 · see citing works
The distribution and protection of intertidal habitats in Australia
Dhanjal‐Adams, K. L., Hanson, J. O., Murray, N., Phinn, S., Wingate, V. R., Mustin, K., Lee, J. R., Allan, J. R., Cappadonna, J. L., Studds, C. E., et al. (2016) · Emu - Austral Ornithology · doi:10.1071/mu15046 · Cited by 45 · see citing works
LANDFIRE Remap Prototype Mapping Effort: Developing a New Framework for Mapping Vegetation Classification, Change, and Structure
Picotte, J. J., Dockter, D., Long, J. B., Tolk, B., Davidson, A., & Peterson, B. (2019) · Fire · doi:10.3390/fire2020035 · Cited by 45 · see citing works
Observation of typhoon-induced seagrass die-off using remote sensing
Kim, K., Choi, J.-K., Ryu, J., Jeong, H. J., Lee, K., Park, M. G., & Kim, K. Y. (2015) · Estuarine Coastal and Shelf Science · doi:10.1016/j.ecss.2014.12.036 · Cited by 43 · see citing works
Monitoring tidal flat dynamics affected by human activities along an eroded coast in the Yellow River Delta, China
Fan, Y., Chen, S., Zhao, B., Yu, S., Ji, H., & Jiang, C. (2018) · Environmental Monitoring and Assessment · doi:10.1007/s10661-018-6747-7 · Cited by 43 · see citing works
Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands
Alonso-Benito, A., Arroyo, L. A., Arbelo, M., & Hernández-Leal, P. A. (2016) · Remote Sensing · doi:10.3390/rs8080669 · Cited by 42 · see citing works
Stratified even sampling method for accuracy assessment of land use/land cover classification: a case study of Beijing, China
Dong, S., Chen, Z., Gao, B., Guo, H., Sun, D., & Pan, Y. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1739349 · Cited by 41 · see citing works
Global data and tools for local forest cover loss and REDD+ performance assessment: Accuracy, uncertainty, complementarity and impact
Bos, A., Sy, V. D., Duchelle, A. E., Herold, M., Martius, C., & Tsendbazar, N. (2019) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2019.04.004 · Cited by 40 · see citing works
Machine-learning-based damage identification methods with features derived from moving principal component analysis
Zhang, G., Tang, L., Liu, Z., Zhou, L., Liu, Y., & Jiang, Z. (2020) · Mechanics of Advanced Materials and Structures · doi:10.1080/15376494.2019.1710308 · Cited by 39 · see citing works
Classification Assessment Tool: A program to measure the uncertainty of classification models in terms of class-level metrics
Szabó, S., Holb, I. J., Molnár, V. É., Szatmári, G., Singh, S. K., & Abriha, D. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111468 · Cited by 37 · see citing works
Exploring geo-tagged photos for land cover validation with deep learning
Xing, H., Meng, Y., Wang, Z., Fan, K., & Hou, D. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.04.025 · Cited by 37 · see citing works
Integrated assessment of deforestation drivers and their alignment with subnational climate change mitigation efforts
Bos, A., Sy, V. D., Duchelle, A. E., Atmadja, S., de Bruin, S., Wunder, S., & Herold, M. (2020) · Environmental Science & Policy · doi:10.1016/j.envsci.2020.08.002 · Cited by 37 · see citing works
Land Use, anthropogenic disturbance, and riverine features drive patterns of habitat selection by a wintering waterbird in a semi-arid environment
Boggie, M. A., Collins, D. P., Donnelly, J., & Carleton, S. A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0206222 · Cited by 36 · see citing works
Has China’s Natural Forest Protection Program Protected Forests?—Heilongjiang’s Experience
Shi, M., Qi, J., & Yin, R. (2016) · Forests · doi:10.3390/f7100218 · Cited by 35 · see citing works
Evaluating the Use of an Object-Based Approach to Lithological Mapping in Vegetated Terrain
Grebby, S. R., Field, E., & Tansey, K. J. (2016) · Remote Sensing · doi:10.3390/rs8100843 · Cited by 35 · see citing works
Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor
Schultz, M., Shapiro, A., Clevers, J. G. P. W., Beech, C., & Herold, M. (2018) · Remote Sensing · doi:10.3390/rs10111850 · Cited by 35 · see citing works
Could land surface phenology be used to discriminate Mediterranean pine species?
Aragonés, D., Rodriguez-Galiano, V. F., Caparros-Santiago, J. A., & Navarro-Cerrillo, R. M. (2019) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2018.11.003 · Cited by 35 · see citing works
Why is determining peri-urban area boundaries critical for sustainable urban development?
Mortoja, M. G., & Yiğitcanlar, T. (2021) · Journal of Environmental Planning and Management · doi:10.1080/09640568.2021.1978405 · Cited by 37 · see citing works
An Intercomparison of Sentinel-1 Based Change Detection Algorithms for Flood Mapping
Tupas, M. E., Roth, F., Bauer-Marschallinger, B., & Wagner, W. (2023) · Remote Sensing · doi:10.3390/rs15051200 · Cited by 34 · see citing works
Remote sensing-derived national land cover land use maps: a comparison for Malawi
Haack, B., Mahabir, R., & Kerkering, J. (2014) · Geocarto International · doi:10.1080/10106049.2014.952355 · Cited by 33 · see citing works
Rapid Loss of Tidal Flats in the Yangtze River Delta since 1974
Li, X., Zhang, X., Qiu, C., Duan, Y., Liu, S., Chen, D., Zhang, L., & Zhu, C. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17051636 · Cited by 33 · see citing works
Quantitative experimental comparison of single-beam, sidescan, and multibeam benthic habitat maps
Schimel, A. C. G., Healy, T. R., Johnson, D., & Immenga, D. (2010) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsq102 · Cited by 33 · see citing works
More accurate less meaningful? A critical physical geographer’s reflection on interpreting remote sensing land-use analyses
Braun, A. (2021) · Progress in Physical Geography Earth and Environment · doi:10.1177/0309133321991814 · Cited by 32 · see citing works
Expansion of Eucalyptus Plantation on Fertile Cultivated Lands in the North-Western Highlands of Ethiopia
Molla, G., Addisie, M. B., & Ayele, G. T. (2023) · Remote Sensing · doi:10.3390/rs15030661 · Cited by 31 · see citing works
A framework for the geometric accuracy assessment of classified objects
Möller, M., Birger, J., Gidudu, A., & Gläßer, C. (2013) · International Journal of Remote Sensing · doi:10.1080/01431161.2013.845319 · Cited by 31 · see citing works
Forest cover and disturbance changes, and their driving forces: A case study in the Ore Mountains, Czechia, heavily affected by anthropogenic acidic pollution in the second half of the 20th century
Kupková, L., Potůčková, M., Lhotáková, Z., & Albrechtová, J. (2018) · Environmental Research Letters · doi:10.1088/1748-9326/aadd2c · Cited by 31 · see citing works
Spatial pattern of pika holes and their effects on vegetation coverage on the Tibetan Plateau: An analysis using unmanned aerial vehicle imagery
Tang, Z., Zhang, Y., Cong, N., Wimberly, M. C., Wang, L., Huang, K., Li, J., Zu, J., Zhu, Y., & Chen, N. (2019) · Ecological Indicators · doi:10.1016/j.ecolind.2019.105551 · Cited by 31 · see citing works
Sample-Efficient Safety Assurances Using Conformal Prediction
Luo, R., Zhao, S., Kuck, J., Ivanovic, B., Savarese, S., Schmerling, E., & Pavone, M. (2022) · Springer proceedings in advanced robotics · doi:10.1007/978-3-031-21090-7_10 · Cited by 31 · see citing works
Shifting climate zones and expanding tropical and arid climate regions across Kenya (1980–2020)
Lawrence, T. J., Vilbig, J. M., Kangogo, G. K., Fèvre, E. M., Deem, S. L., Gluecks, I., Sagan, V., & Shacham, E. (2023) · Regional Environmental Change · doi:10.1007/s10113-023-02055-w · Cited by 31 · see citing works
Latent Class Modeling for Site- and Non-Site-Specific Classification Accuracy Assessment Without Ground Data
Foody, G. M. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2174156 · Cited by 30 · see citing works
Spatial Stratification Method for the Sampling Design of LULC Classification Accuracy Assessment: A Case Study in Beijing, China
Dong, S., Guo, H., Chen, Z., Pan, Y., & Gao, B. (2022) · Remote Sensing · doi:10.3390/rs14040865 · Cited by 29 · see citing works
Reading light: leaf spectra capture fine‐scale diversity of closely related, hybridizing arctic shrubs
Stasinski, L., White, D. M., Nelson, P. R., Ree, R. H., & Meireles, J. E. (2021) · New Phytologist · doi:10.1111/nph.17731 · Cited by 28 · see citing works
Remote sensing mapping of the regeneration of coastal natural habitats in Singapore: Implications for marine conservation in tropical cities
Tan, Y. H. J., Tham, J. K. Q., Paul, A., Rana, U., Ang, H. P., Nguyen, N., Yee, A. T. K., Leong, B. P. I., Drummond, S., & Tun, K. (2022) · Singapore Journal of Tropical Geography · doi:10.1111/sjtg.12454 · Cited by 27 · see citing works
A scale-sensitive framework for the spatially explicit accuracy assessment of binary built-up surface layers
Uhl, J. H., & Leyk, S. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2022.113117 · Cited by 27 · see citing works
Better, Not Just More: Data-centric machine learning for Earth observation
Roscher, R., Rußwurm, M., Gevaert, C. M., Kampffmeyer, M., dos Santos, J. A., Vakalopoulou, M., Hänsch, R., Hansen, S., Nogueira, K., Prexl, J., et al. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3470986 · Cited by 26 · see citing works
Assessing the potential applications of Landsat image archive in the ecological monitoring and management of a production mangrove forest in Malaysia
Aziz, A. A., Phinn, S., Dargusch, P., Omar, H., & Arjasakusuma, S. (2015) · Wetlands Ecology and Management · doi:10.1007/s11273-015-9443-1 · Cited by 25 · see citing works
Comparing Three Methods of Selecting Training Samples in Supervised Classification of Multispectral Remote Sensing Images
Zhang, H., He, J., Chen, S., Zhan, Y., Bai, Y., & Qin, Y. (2023) · Sensors · doi:10.3390/s23208530 · Cited by 24 · see citing works
Contemporary and historical classification of crop types in Arizona
Hartfield, K. A., Marsh, S. E., Kirk, C. D., & Carrière, Y. (2013) · International Journal of Remote Sensing · doi:10.1080/01431161.2013.793861 · Cited by 24 · see citing works
Consideration of Landsat-8 Spectral Band Combination in Typical Mediterranean Forest Classification in Halkidiki, Greece
Elhag, M. (2017) · Open Geosciences · doi:10.1515/geo-2017-0036 · Cited by 24 · see citing works
Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning
Varga, O. G., Kovács, Z., Bekő, L., Burai, P., Szabó, Z. C., Holb, I. J., Ninsawat, S., & Szabó, S. (2021) · Remote Sensing · doi:10.3390/rs13050857 · Cited by 23 · see citing works
Spatiotemporal analysis of land use/cover changes in Nanchang area, China
Zhang, Y., & Xu, B. (2014) · International Journal of Digital Earth · doi:10.1080/17538947.2014.894145 · Cited by 23 · see citing works
Statistical comparison of simple and machine learning based land use and land cover classification algorithms: A case study
Rawat, K. S., Kumar, S., & Garg, N. (2024) · Journal of Water Management Modeling · doi:10.14796/jwmm.h524 · Cited by 22 · see citing works
Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series
Karasiak, N., Dejoux, J.-F., Fauvel, M., Willm, J., Monteil, C., & Sheeren, D. (2019) · Remote Sensing · doi:10.3390/rs11212512 · Cited by 22 · see citing works
Vegetation type and land cover mapping in a semi-arid heterogeneous forested wetland of India: comparing image classification algorithms
Deval, K., & Joshi, P. K. (2021) · Environment Development and Sustainability · doi:10.1007/s10668-021-01596-6 · Cited by 22 · see citing works
Developing a National Dataset of Bicycle Infrastructure for Canada Using Open Data Sources
Ferster, C., NELSON, T. A., Manaugh, K., Beairsto, J., Laberee, K., & Winters, M. (2022) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/23998083231159905 · Cited by 22 · see citing works
Remote Sensing Based Two-Stage Sampling for Accuracy Assessment and Area Estimation of Land Cover Changes
Gallaun, H., Steinegger, M., Wack, R., Schardt, M., Kornberger, B., & Schmitt, U. (2015) · Remote Sensing · doi:10.3390/rs70911992 · Cited by 22 · see citing works
Multiscale habitat relationships of a habitat specialist over time: The case of ocelots in Texas from 1982 to 2017
Veals, A. M., Holbrook, J. D., Blackburn, A., Anderson, C. J., DeYoung, R. W., Campbell, T. A., Young, J. H., & Tewes, M. E. (2022) · Ecosphere · doi:10.1002/ecs2.4204 · Cited by 21 · see citing works
Towards reliable monitoring of grass species in nature conservation: Evaluation of the potential of UAV and PlanetScope multi-temporal data in the Central European tundra
Kupková, L., Červená, L., Potůčková, M., Lysák, J., Roubalová, M., Hrázský, Z., Březina, S., Epstein, H. E., & Müllerová, J. (2023) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113645 · Cited by 21 · see citing works

The Effects of Sample Size Selection on Classification Accuracy
Sarp, G., & Erener, A. (2012) · Cited by 1 · see citing works
The Sample Size Influence in the Accuracy of the Image Classification of the Remote Sensing
da Costa, T. C. E. C., & Brites, R. S. (2004) · DOAJ (DOAJ: Directory of Open Access Journals)
STUDY OF SAMPLING METHODS FOR ACCURACY ASSESSMENT OF CLASSIFIED REMOTELY SENSED DATA
(2015)
Accuracy assessment model for classification result of remote sensing image based on spatial sampling
Huang, D., Xu, S., Sun, J., Liang, S., Song, W., & Wang, Z. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.046023 · Cited by 43 · see citing works
Remote Sensing: Statistical Testing of Thematic Map Accuracy
van Genderen, J. L., Lock, B. F., & Vass, P. A. (1978) · Remote Sensing of Environment · doi:10.1016/0034-4257(78)90003-2 · Cited by 92 · see citing works

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 › Engineering › Media Technology › Remote-Sensing Image Classification
Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › 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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Sample size determination for image classification accuracy assessment and comparison

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