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A Comparison of Accuracy Measures for Remote Sensing Image Classification: Case Study In An Amazonian Region Using Support Vector Machine
Graziela Balda Scofield, Eliana Pantaleao, Rogerio Galante Negri
Pages - 11 - 21     |    Revised - 31-1-2015     |    Published - 28-2-2015
Volume - 9   Issue - 1    |    Publication Date - January / February 2015  Table of Contents
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KEYWORDS
Image Classification, Accuracy Measures, Category-level, Map-level, Comparison.
ABSTRACT
This work investigated the consistency of both the category-level and the map-level accuracy measures for different scenarios and features using Support Vector Machine. It was verified that the classification scenario and the features adopted have not influenced the accuracy measure consistency and all accuracy measures are highly positively correlated.
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1 Tiwari, L. K., Sinha, S. K., Saran, S., Tolpekin, V. A., & Raju, P. L. (2016). Forest encroachment mapping in Baratang Island, India, using maximum likelihood and support vector machine classifiers. Journal of Applied Remote Sensing, 10(1), 016016-016016.
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1 Y. E. Shimabukuro, R. Almeida-Filho, T. M. Kuplich, R. M. Freitas, “Mapping and monitoring land cover in Corumbiara area, BrazilianAmazonia, using JERS-1 SAR multitemporal data”, IEEE International Geoscience and Remote Sensing Symposium, Barcelona, 2007.
2 A. Niedermeier, S. Lehner, J. Sander, “Monitoring big river estuaries using SAR images”, IEEE International Geoscience and Remote Sensing Symposium, Sydney, 2001.
3 K. Whitehead, B. Moorman, P. Wainstein, “Determination of variations in glacier surface movements through high resolution interferometry: Bylot Island, Canada”, IEEE International Geoscience and Remote Sensing Symposium, Cape Town, 2009.
4 A. Chesnel, R. Binet, L.Wald, “Object oriented assessment of damage due to natural disaster using very high resolution images”, IEEE International Geoscience and Remote Sensing Symposium, Barcelona, 2007.
5 M. Kasanko, V. Sagris, C. Lavalle, J. I. Barredo, L. Petrov, K. Steinnocher, W. Loibl, C. Hoffmann, “GEOLAND spatial planning observatory: How remote sensing data can serve the needs of urban and regional planning”, Urban Remote Sensing Joint Event, Paris, 2007.
6 C. Liu, P. Fraizer, L. Kuman, “Comparative assessment of the measures of thematic classification accuracy,” Remote Sensing of Environment. vol. 107, pp. 606-616, 2007.
7 J. A. Cohen, “Coefficient of agreement of nominal scales,” Educational and Psychological Measurement. vol. 20, pp. 37-46, 1960.
8 M. Story, R. G. Congalton. “Accuracy assessment: a user’s perspective,” Photogrammetric Engineering and Remote Sensing. vol. 52, pp. 397-399, 1986.
9 G. H. Rosenfield, K. A.Fitzpatrick-Lins, “Coefficient of agreement as a measure of thematic classification accuracy,” Photogrammetric Engineering and Remote Sensing. vol. 52, pp. 223-227, 1986.
10 S. V. Stehman, “Selecting and interpreting measures of thematic classification accuracy,” Remote Sensing of Environment. vol. 62, pp. 77-89, 1997.
11 T. Fung, E. Ledrew, “The determination of optimal threshold levels for change detection using various accuracy indices,” Photogrammetric Engineering and Remote Sensing. vol. 54, pp. 1449-1454, 1988.
12 N. M. Short, “The LANDSAT tutorial workbook: basics of satellite Remote Sensing”. National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1982.
13 U. A. Hellden, “Test of LANDSAT-2 imagery and digital data for thematic mapping illustrated by an environmental study in northern Kenya”, Lund University Natural Geography Institute Report, 1980.
14 J. T. Finn, “Use of the average mutual information index in evaluating classification error and consistency,” International Journal of Geographical Information Systems. vol. 7, pp. 349- 366, 1993.
15 R. G. Congalton, “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data,” Remote Sensing of Environment. vol. 37, pp. 35-46, 1991.
16 J. R. Jansen, Introductory Digital Image Processing: A Remote Sensing Perspective, Pearson Prentice Hall, Upper Saddle River, 2005.
17 ZEE BR-163, 2011. Zoneamento ecológico-econômico da rodovia BR-163, Access in December 2011. http://zeebr163.cpatu.embrapa.br.
18 S. Theodoridis, K. Koutrombas, Pattern Recognition, Academic Press, San Diego, 2006.
19 A. R. Webb. Statistical Pattern Recognition, Jhon Wiley and Sons, Chichester, 2002.
20 J. G. P. W. Clevers, “The derivation of a simplified reflectance model for the estimation of leaf area index,” Remote Sensing of Environment. vol. 35, pp. 53-70, 1988.
21 R. M. Haralick, K. Shanmugam, I. Dinsten, “Texture features for image classification,” IEEE Transactions on Systems, Manchine and Cybernetics. vol. 3, pp. 610-622, 1973.
22 R. C. Gonzales, R. E. Woods. Digital Image Processing, Prentice Hall, California, 2007.
23 A. A. Green, M. Berman, P. Switzer, M. D. Craig, “A transformation for ordering multispectral data in terms of image quality with implications for noise removal,” IEEE Transactions on Geoscience and Remote Sensing. vol. 26, pp. 65-74, 1998.
Dr. Graziela Balda Scofield
Centro Nacional de Monitoramento e Alertas de Desastres Naturais - Brazil
graziela.scofield@cemaden.gov.br
Dr. Eliana Pantaleao
Universidade Federal de Uberlandia - Brazil
Dr. Rogerio Galante Negri
Sao Paulo State University (UNESP) - Brazil