Estimation of fuzzy error matrix accuracy measures under stratified random sampling

Photogrammetric Engineering and Remote Sensing

Volumn 73, Issue 2, 2007, Pages 165-173

  Stehman, Stephen V ^a   Arora, Manoj K ^b   Kasetkasem, Teerasit ^c   Varshney, Pramod K ^d  

^a STATE UNIVERSITY OF NEW YORK   (United States)

^b INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

^c KASETSART UNIVERSITY   (Thailand)

^d Syracuse University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; DATA ACQUISITION; ERROR ANALYSIS; SAMPLING;

FUZZY ERROR MATRIX ACCURACY MEASURES; STRATIFIED RANDOM SAMPLING; STRATIFIED SAMPLING ESTIMATORS;

FUZZY SETS;

ACCURACY ASSESSMENT; CLASSIFICATION; DATA ACQUISITION; ESTIMATION METHOD; FUZZY MATHEMATICS; MATRIX; SIMULATION;

EID: 33846840792     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.73.2.165     Document Type: Article

References (16)

1. Binaghi, E., P.A. Brivio, P. Ghezzi, and A. Rampini, 1999. A fuzzy set-based accuracy assessment of soft classification, Pattern Recognition Letters, 20:935-948.
2. rd edition, John Wiley & Sons, New York, New York.
3. Finn, J.T., 1993. Use of the average mutual information index in evaluating classification error and consistency, International Journal of Geographical Information Systems, 7:349-366.
4. Foody, G.M., 1995. Cross-entropy for the evaluation of the accuracy of a fuzzy land cover classification with fuzzy ground data, ISPRS Journal of Photogrammetry and Remote Sensing, 50:2-12.
5. Foody, G.M., 2002. Status of land cover classification accuracy assessment, Remote Sensing of Environment, 80:185-201.
6. Foody, G.M., and M.K. Arora, 1996. Incorporating mixed pixels in the training, allocation and testing stages of supervised classification, Pattern Recognition Letters, 17:1389-1398.
7. Foody, G.M., N.A. Campbell, N.M. Trodd, and T.F. Wood, 1992. Derivation and applications of probabilistic measures of class membership from maximum likelihood classification, Photogrammetric Engineering & Remote Sensing, 58:1335-1341.
8. Maselli, F., C. Conese, and L. Petkov, 1994. Use of probability entropy for the estimation and graphical representation of the accuracy of maximum likelihood classifications, ISPRS Journal of Photogrammetry and Remote Sensing, 49:13-20.
9. Maselli, F., A. Rodolf, and C. Conese, 1996. Fuzzy classification of spatially degraded Thematic Mapper data for the estimation of sub-pixel components, International Journal of Remote Sensing, 17:537-551.
10. Myeong, S., D.J. Nowak, P.F. Hopkins, and R.H. Brock, 2003. Urban cover mapping using digital, high-spatial resolution aerial imagery, Urban Ecosystems, 5:243-256.
11. Ricotta, C., 2004. Evaluating the classification accuracy of fuzzy thematic maps with a simple parametric measure, International Journal of Remote Sensing, 25:2169-2176.
12. Särndal, C.E., B. Swensson, and J. Wretman, 1992. Model-Assisted Survey Sampling, Springer-Verlag, New York.
13. Schowengerdt, R.A., 1997. Remote Sensing: Models and Methods for Image Processing, Academic Press, San Diego, California.
14. Stehman, S.V., 2000. Practical implications of design-based sampling inference for thematic map accuracy assessment, Remote Sensing of Environment, 72:35-45.
15. Stehman, S.V., and R.L. Czaplewski, 1998. Design and analysis for thematic map accuracy assessment: Fundamental principles, Remote Sensing of Environment, 64:331-344.
16. Wang, F., 1990. Fuzzy supervised classification of remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 28:194-201.