Comparing thematic maps based on map value

International Journal of Remote Sensing

Volumn 20, Issue 12, 1999, Pages 2347-2366

  Stehman, S V ^a  

^a STATE UNIVERSITY OF NEW YORK   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; MAPPING; MAPS;

MAP VALUE; THEMATIC MAPS;

REMOTE SENSING;

COMPARATIVE STUDY; MAPPING; THEMATIC MAPPING;

EID: 0032826392     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/014311699212065     Document Type: Article

References (37)

1. Agbu, P. A., and Nizeyimana, E.,1991, Comparisons between spectral mapping units derived from SPOT image texture and field soil map units.Photogrammetric Engineering and Remote Sensing,57, 397-405.
2. Agresti, A.,1996, An Introduction to Categorical Data Analysis(New York: John Wiley &Sons).
3. Aickin, M., 1990. Maximum likelihood estimation of agreement in the constant predictive probability model, and its relation to Cohen's kappa.Biometrics,46, 293-302.
4. Aronofi, S., 1982. Classification accuracy: a user approach.Photogrammetric Engineering and Remote Sensing,48, 1299-1307.
5. Campbell, J. B., 1987, Introduction to Remote Sensing(New York: Guilford Press).
6. Conese, C., and Maselli, F., 1992, Use of error matrices to improve area estimates with maximum likelihood classification procedures.Remote Sensing of Environment.40.113-124.
7. Congalton, R. G.,1991, Areview of assessing the accuracy of classifications of remotely sensed data.Remote Sensing of Environment,37, 35-46.
8. Congalton, R. G., Oderwald, R. G., andMead, R. A., 1983, Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques.Photogrammetric Engineering and Remote Sensing,49, 1671-1678.
9. Dicks, S. E., and Lo, T. H. C., 1990, Evaluation of thematic map accuracy in a land-use and land-cover mapping programPhotogrammetric Engineering and Remote Sensing,56, 1247-1252
10. Dikshit, O., and Roy, D.P., 1996, An empirical investigation of image resampling effects upon the spectral and textural supervised classification of a high spatial resolution multispectral image.Photogrammetric Engineering and Remote Sensing,62, 1085-1092
11. Fitzgerald, R. W., andLees, B. W., 1994, Assessing the classification accuracy of multiresource remote sensing data.Remote Sensing of Environment,47, 362-368.
12. Fleiss, J. L., 1981, Statistical Methods for Rates and Proportions,2nd edn (New York: John Wiley &Sons).
13. Foody, G.M., 1992, On the compensation for chance agreement in image classification accuracy assessment.Photogrammetric Engineering and Remote Sensing,58,1459-1460.
14. Fung, T., andLeDrew, E., 1988, The determination of optimal threshold levels for change detection using various accuracy indices.Photogrammetric Engineering and Remote Sensing,54, 1449-1454.
15. Gong, P., andHowarth, P. J., 1990, An assessment of some factors influencing multispectral land-cover classification.Photogrammetric Engineering and Remote Sensing,56. 597-603.
16. Hardin, P. J., andShumway, J. M. 1997, Statistical significance and normalized confusion matrices.Photogrammetric Engineering and Remote Sensing,63, 735-740.
17. Harris, P. M., and Ventura, S. J., 1995, The integration of geographic data with remotely sensed imagery to improve classification in an urban area.Photogranunetric Engineering and Remote Sensing,61, 993-998.
18. Kontoes, C. C., and Rokos, D., 1996, The integration of spatial context information in an experimental knowledge-based system and the supervised relaxation algorithm—two successful approaches to improving SPOT-XS classification.International Journal of Remote Sensing,17, 3093-3106.
19. Lark, R. M., 1995, Components of accuracy of maps with special reference to discriminant analysis on remote sensor data.International Journal of Remote Sensing,16, 1461-1480.
20. Lunetta, R. S., Congalton, R.G., Fenstermaker, L. K., Jensen, J. R., McGwire, K. C, and Tinney, L.R., 1991, Remote sensing and geographic information system data integration: error sources and research issues.Photogrammetric Engineering and Remote Sensing,57, 677-687.
21. Ma, Z., andRedmond, R.L., 1995, Tau coefficients for accuracy assessment of classification of remote sensing data.Photogrammetric Engineering and Remote Sensing,61,435-439.
22. Marsh, S. E., Walsh, J. L., andSobrevila, C, 1994, Evaluation of airborne video data for land-cover classification accuracy assessment in an isolated Brazilian forest.Remote Sensing of Environment,48. 61-69.
23. McGwire, K. C., Fairbanks, D. H. K., andEstes, J.E.,1992, Examining regional vegetation associations using multi-temporal AVHRR imagery.American Society of Photo-grammetry and Remote Sensing Technical Papers, Volume 1, 304-311.
24. McGwire, K. G, Estes, J.E., andStar, J. L., 1996, A comparison of maximum likelihood-based supervised classification strategies.Geocar to International,11, 3-13.
25. Naesset, E., 1996, Use of the weighted Kappa coefficient in classification error assessment of thematic maps.International Journal of Geographic Information Systems,10. 591-604.
26. Riley, R.H., Phillips, D. L., Schuft, M. J., andGarcia, M. C., 1997, Resolution and eiror in measuring land-cover change: effects on estimating net carbon release from Mexican terrestrial ecosystems.International Journal of Remote Sensing,18, 121-137.
27. Rolley, R.E., and Warde, W. D. 1985. Bobcat habitat use in southeastern Oklahoma.Journal of Wildlife Management,49, 913-920.
28. Rosenheld, G. H. and Fitzpatrick-Lins, K., 1986. A coefficient of agreement as a measure of thematic classification accuracy.Photogrammetric Engineering and Remote Sensing.52, 223-227.
29. San Miguel-Ayanz, J., and Biging, G. S., 1996.Aniterative classification approach for mapping natural resources from satellite imagery.International Journal of Remote Sensing,17, 957-981.
30. San Miguel-Ayanz, J., and Biging, G. S.,1997. Comparison of single-stage and multi-stage classification approaches for cover type mapping with TM and SPOT data.Remote Sensing of Environment,59, 92-104.
31. Sarndal, C. E., Swensson, B., and Wrliman, J., 1992, Model-Assisted Survey Sampling (New York: Springer-Verlag).
32. Stehman, S.V., 1997. Selecting and interpreting measures of thematic classification accuracy.Remote Sensing of Environment,62, 77-89.
33. Stenback, J. M., and Congalton, R. G., 1990, Using thematic mapper imagery to examine forest understory.Photogrammetric Engineering and Remote Sensing,56. 1285-1290.
34. Story, M., and Congalton, R. G., 1986, Accuracy assessment: a user’s perspective.Photogrammetric Engineering and Remote Sensing,52, 397-399.
35. Treitz, P. M., Howarth, P. J., Suffling, R. C., and Smith, P. 1992, Application of detailed ground information to vegetation mapping with high spatial resolution digital imagery.Remote Sensing of Environment,42, 65-82.
36. Wolter, K. 1985, Introduction to Variance Estimation(New York: Springer-Verlag).
37. Zhuang, X., Engel, B.A., Xiong, X., and Johannsen, C. J.,1995, Analysis of classification results of remotely sensed data and evaluation of classification algorithms.Photogrammetric Engineering and Remote Sensing,61, 427-433.