Two-stage subpixel impervious surface coverage estimation: Comparing classification and regression trees and artificial neural networks

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 9244, Issue , 2014, Pages

  Bernat, Katarzyna ^a   Drzewiecki, Wojciech ^a  

^a AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Poland)

Author keywords

Artificial neural networks; Classification and regression trees; Dobczyce Reservoir; Impervious surfaces; Landsat TM; Subpixel classification

Indexed keywords

ARTIFICIAL INTELLIGENCE; CATCHMENTS; DECISION TREES; FORESTRY; LEARNING ALGORITHMS; LEARNING SYSTEMS; MAPS; MEAN SQUARE ERROR; NEURAL NETWORKS; PIXELS; RANDOM ERRORS; REGRESSION ANALYSIS; REMOTE SENSING;

ACCURACY COMPARISONS; CLASSIFICATION AND REGRESSION TREE; IMPERVIOUS SURFACE; LANDSAT TM; RANDOM FOREST METHODS; RESERVOIR CATCHMENTS; ROOT MEAN SQUARE ERRORS; SUB-PIXEL CLASSIFICATION;

SIGNAL PROCESSING;

NEURAL NETWORKS; RADAR; REGRESSION ANALYSIS; TREES;

EID: 84923039381     PISSN: 0277786X     EISSN: 1996756X     Source Type: Conference Proceeding    
DOI: 10.1117/12.2067308     Document Type: Conference Paper

References (31)

1. Weng, Q., "Remote sensing of impervious surface in the urban areas: Requiremants, methods and trends", Remote Sensing of Environment 117, 34-49 (2012).
2. Bauer, M.E., Heiner, N.J., Doyle, J.K., Yuan, F., "Impervious surface mapping and change monitoring using landsat remote sensing", ASPRS Annual Conference Proceedings (2004) (unpaginated CD-ROM)
3. Chormanski, J., Van de Voorde, T., De Roeck, T., Batelaan, O., Canters, F., "Improving Distributed Runoff Prediction in Urbanized Catchments with Remote Sensing based Estimates of Impervious Surface Cover", Sensors 8, 910-932 (2008).
4. Jat, M.K., Garg, P.K., Khare, D., "Monitoring and modelling of urban sprawl using remote sensing and GIS techniques", International Journal of Applied Earth Observation and Geoinformation 10, 26-43 (2008).
5. Powell, S.L., Cohen, W.B., Yang, Y., Pierce, J.D., Alberti, M., "Quantification of impervious surface in the Snohomish Water Resources Inventory Area of Western Washington from 1972-2006", Remote Sensing of Environment 112, 1895-1908 (2008).
6. Weng, Q, Hu, X, "Medium Spatial Resolution Satellite Imagery for Estimating and Mapping Urban Impervious Surfaces Using LSMA and ANN", IEEE Transactions on Geoscience and Remote Sensing, 46(8), 2397-2402 (2008).
7. Hu, X., Weng, Q., "Estimating impervious surface from medium spatial resolution imagery using the selforganizing map and multi-layer perceptron neural networks", Remote Sensing of Environment 113, 2089-2102 (2009).
8. Civco, D. L. And Hurd J. D., "Impervious surface mapping for the state of Connecticut", Proc. of the 1997 ASPRS Annual Conference (1997).
9. Flanagan, M. And Civco D. L., "Subpixel impervious surface mapping", Proc. of the 2001 ASPRS Annual Convention 23 (2001).
10. Lee, S., and Lathrop, R. G., "Subpixel analysis of Landsat ETM+ using self-organizing map (SOM) neural networks for urban land cover characterization", Geoscience and Remote Sensing, IEEE Transactions on 44(6), 1642-1654 (2006).
11. Ji, C.Y., "Land-use classification of remotely sensed data using Kohonen self-organizing feature map neural networks", Photogrammetric Engineering & Remote Sensing 66, 1451-1460 (2000).
12. Rulequest, "Data Mining with Cubist", RuleQuest Research Pty Ltd. www.rulequest.com/cubist-info.html (20 August 2014).
13. 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 73, 337-341 (2007).
14. Xian, G., "Mapping Impervious Surfaces Using Classification and Regression Tree Algorithm" [in:] Weng, Q., [Remote Sensing of Impervious Surfaces], CRC Press, Taylor & Francis Group, Boca Raton-London-New York, 39-58 (2008).
15. Drzewiecki, W., "Sub-pixel classification of middle-resolution satellite images-evaluation of regression trees applicability to monitor impervious surfaces coverage", Geomatics and Environmental Engineering 4(4), 61-75 (2010).
16. Drzewiecki, W., Osak, A., "Application of Landsat Imagery Based Vegetation Indices to Imperviousness Index Mapping", Geomatics and Environmental Engineering 3(4), 43-52 (2009).
17. Breiman, L., "Random Forest", Machine Learning 45, 5-32 (2001).
18. Walton, J. T., "Subpixel Urban Land Cover Estimation: Comparing Cubist, Random Forest and Support Vector Regression", Photogrammetric Engineering & Remote Sensing 75(10), 1213-1222 (2008).
19. 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 86, 100-110 (2013).
20. Shao, Y., Lunetta, R. S., "Comparison of support vector machine, neural network and CART algorithm for the land-cover classification using limited training data points", ISPRS J. Photogramm 70, 78-87 (2012).
21. Kavzoglu, T, Mather, P. M., "The use of backpropagating artificial neural networks in land cover classification", International Journal of Remote Sensing, 24(23), 4907-4938 (2003).
22. Yuan, F., Bauer, M. E., Heinert, N. J., Holden, G. R., "Multi-level land cover mapping of the Twin Cities (Minnesota) metropolitan area with multi-seasonal Landsat TM/ETM+ data", Geocarto International, 20(2), 5-13 (2005).
23. Xu, H., "Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery", International Journal of Remote Sensing 27(14), 3025-2033 (2006).
24. Deng, Ch., Wu, Ch., "BCI: A biophysical composition index for remote sensing of urban environments", Remote Sensing of Environment 127, 247-259 (2012).
25. Lewinski, S., "Rozpoznanie form pokrycia i uzytkowania ziemi na zdjeciu satelitarnym Landsat ETM+ metoda klasyfikacji obiektowej", Roczniki Geometyki 4( 3), 139-150 (2006).
26. Kuhn, M., Johnson, K., [Applied Predictive Modeling], Springer Science + Business Media, New York (2013).
27. Hecht-Nielsen, R., "Theory of the back propagation neural network", Neural Networks, IJCNN., International Joint Conference on, IEEE, 593-605 (1989).
28. Qian, N., "On the momentum term in gradient descent learning algorithms", Neural networks 12(1), 145-151 (1999).
29. 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., Steinberg, D., "Top 10 algorithms in data mining", Knowledge and Information Systems 14( 1), 1-37 (2008).
30. Quinlan, J.R., [C4.5: Programms for Machine Learning], Morgan Kaufmann Publishers Inc., San Mateo (1993).
31. Jacobs, R. A., "Increased rates of convergence through learning rate adaptation", Neural networks 1(4), 295-307 (1988).