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TWO-STAGE SUBPIXEL IMPERVIOUS SURFACE COVERAGE ESTIMATION: COMPARING C 5.0/CUBIST AND RANDOM FOREST
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Peters N.E. Effects of urbanization on stream water quality in the city of Atlanta, Georgia, USA. Hydrological Processes, vol. 23, 2009, pp. 2860-2878.
Arnold C.L. Jr. & Gibbons C.J. Impervious surface coverage: the emergence o f a key environmental indicator, Journal of the American Planning Association, vol. 62, 1996, pp. 243-258.
Weng Q. Remote sensing of impervious surface in the urban areas: Requirements, methods and trends, Remote Sensing if Environment, vol. 117, 2012, pp. 34-49.
Xu M., Watanachaturaporn P., Varshney P.K., Arora M.K. Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment, vol. 97, 2005, pp. 322-336.
Xian G.: Mapping Impervious Surfaces Using Classification and Regression Tree Algorithm. [in:] Weng Q. (Eds.), Remote Sensing of Impervious Surfaces, CRC Press, Taylor & Francis Group, Boca Raton – London – New York 2008, pp. 39–58.
Rulequest, Data Mining with Cubist, URL: http://www.rulequest.com/cubist- info.html, RuleQuest Research Pty Ltd., St. Ives, NSW (last date accessed: April 2014).
Kuhn M., Johnso n K.: Applied Predictive Modeling, Springer Science + Business Media, New York 2013.
Homer C., Dewitz J., Fry J., Coan M., Hossain N., Larson C., Herold N., McKerrow A., Van Driel J.N., Wickham J. Completion of the 2001 National Land Cover Database for the Conterminous United States. Photogrammetric Engineering and Remote Sensing, vol. 73, 2007, pp. 337-341.
Breiman L. Random Forest, Machine Learning, vol. 45, pp. 5-32, 2001.
Walton J. T. Subpixel Urban Land Cover Estimation: Comparing Cubist, Random Forest and Support Vector Regression, Photogrammetric Engineering & Remote Sensing, vol. 75/issue 10, pp. 1213-1222, 2008.
Deng Ch. Wu Ch. The use of single-date MODIS imagery for estimating large- scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 86, pp. 100-110, 2013. 14th SGEM GeoConference on Informatics, Geoinformatics and Remote Sensing
Xu H.: Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, vol. 27, no. 14, 2006, pp. 3025-2033.
Deng Ch., Wu Ch.: BCI: A biophysical composition index for remote sensing of urban environments, Remote Sensing of Environment, vol. 127, 2012, pp. 247-259.
L ewiński S.: Rozpoznanie form pokrycia i użytkowania ziemi na zdjęciu satelitarnym Landsat ETM+ metodą klasyfikacji obiektowej , Roczniki Geomatyki, vol. 4, no. 3, 2006, pp. 139-150.
Wu X., Kumar V., Quinlan J.R., Ghosh J., Yang Q., Motoda H., McLachlan G.J., Ng A., Liu B., Yu P.S., Zhou Z., Steinbach M., Hand D.J., Dan Steinberg D.: Top 10 algorithms in data mining, Knowledge and Information Systems, vol. 14, no. 1, 2008, pp.1 -37.
Peters N.E. Effects of urbanization on stream water quality in the city of Atlanta, Georgia, USA. Hydrological Processes, vol. 23, 2009, pp. 2860-2878.
Arnold C.L. Jr. & Gibbons C.J. Impervious surface coverage: the emergence o f a key environmental indicator, Journal of the American Planning Association, vol. 62, 1996, pp. 243-258.
Weng Q. Remote sensing of impervious surface in the urban areas: Requirements, methods and trends, Remote Sensing if Environment, vol. 117, 2012, pp. 34-49.
Xu M., Watanachaturaporn P., Varshney P.K., Arora M.K. Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment, vol. 97, 2005, pp. 322-336.
Xian G.: Mapping Impervious Surfaces Using Classification and Regression Tree Algorithm. [in:] Weng Q. (Eds.), Remote Sensing of Impervious Surfaces, CRC Press, Taylor & Francis Group, Boca Raton – London – New York 2008, pp. 39–58.
Rulequest, Data Mining with Cubist, URL: http://www.rulequest.com/cubist- info.html, RuleQuest Research Pty Ltd., St. Ives, NSW (last date accessed: April 2014).
Kuhn M., Johnso n K.: Applied Predictive Modeling, Springer Science + Business Media, New York 2013.
Homer C., Dewitz J., Fry J., Coan M., Hossain N., Larson C., Herold N., McKerrow A., Van Driel J.N., Wickham J. Completion of the 2001 National Land Cover Database for the Conterminous United States. Photogrammetric Engineering and Remote Sensing, vol. 73, 2007, pp. 337-341.
Breiman L. Random Forest, Machine Learning, vol. 45, pp. 5-32, 2001.
Walton J. T. Subpixel Urban Land Cover Estimation: Comparing Cubist, Random Forest and Support Vector Regression, Photogrammetric Engineering & Remote Sensing, vol. 75/issue 10, pp. 1213-1222, 2008.
Deng Ch. Wu Ch. The use of single-date MODIS imagery for estimating large- scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 86, pp. 100-110, 2013. 14th SGEM GeoConference on Informatics, Geoinformatics and Remote Sensing
Xu H.: Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, vol. 27, no. 14, 2006, pp. 3025-2033.
Deng Ch., Wu Ch.: BCI: A biophysical composition index for remote sensing of urban environments, Remote Sensing of Environment, vol. 127, 2012, pp. 247-259.
L ewiński S.: Rozpoznanie form pokrycia i użytkowania ziemi na zdjęciu satelitarnym Landsat ETM+ metodą klasyfikacji obiektowej , Roczniki Geomatyki, vol. 4, no. 3, 2006, pp. 139-150.
Wu X., Kumar V., Quinlan J.R., Ghosh J., Yang Q., Motoda H., McLachlan G.J., Ng A., Liu B., Yu P.S., Zhou Z., Steinbach M., Hand D.J., Dan Steinberg D.: Top 10 algorithms in data mining, Knowledge and Information Systems, vol. 14, no. 1, 2008, pp.1 -37.
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