Estimating urban impervious surfaces from Landsat-5 TM imagery using multilayer perceptron neural network and support vector machine

Journal of Applied Remote Sensing

Volumn 5, Issue 1, 2011, Pages

  Sun, Zhongchang ^a,b   Guo, Huadong ^a   Li, Xinwu ^a   Lu, Linlin ^a   Du, Xiaoping ^a,b  

^a CENTER FOR EARTH OBSERVATION AND DIGITAL EARTH   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

classification; Landsat 5 TM image; multilayer perceptron neural network; percent impervious surface; support vector machine; urban impervious surface

Indexed keywords

CLASSIFICATION (OF INFORMATION); MEAN SQUARE ERROR; MULTILAYER NEURAL NETWORKS; MULTILAYERS; PIXELS; REMOTE SENSING;

CLASSIFICATION PERFORMANCE; COEFFICIENT OF DETERMINATION; LANDSAT-5 TM IMAGES; MULTI LAYER PERCEPTRON NEURAL NETWORKS (MLPNN); MULTI-LAYER PERCEPTRON NEURAL NETWORKS; PERCENT IMPERVIOUS SURFACES; ROOT MEAN SQUARE ERRORS; URBAN IMPERVIOUS SURFACES;

SUPPORT VECTOR MACHINES;

EID: 84855233791     PISSN: None     EISSN: 19313195     Source Type: Journal    
DOI: 10.1117/1.3539767     Document Type: Article

References (43)

1. C. L., Arnold and C. J., Gibbons, " Imperious surface coverage: the emergence of a key environmental indicator, ", J. Am. Plann. Assoc., 62 (2), 243-258 (1996). 10.1080/01944369608975688
2. D. N., Weber and R., Bannerman, " Relationships between impervious surfaces within a watershed and measures of reproduction in fathead minnows (Pimephales promelas), ", Hydrobiologia, 525, 215-228 (2004). 10.1023/B:HYDR.0000038868.46128.e4 (Pubitemid 39408379)
3. T. M., Conway, " Impervious surface as an indicator of pH and specific conductance in the urbanizing coastal zone of New Jersey, USA, ", J. Environ. Manage., 85 (2), 308-316 (2007). 10.1016/j.jenvman.2006.09.023 (Pubitemid 47264021)
4. X., Hu and Q., Weng, " Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks, ", Remote Sens. Environ., 113 (10), 2089-2102 (2009). 10.1016/j.rse.2009.05.014
5. L., Yang, G., Xian, J. M., Klaver and B., Deal, " Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data, ", Photogramm. Eng. Remote Sens., 69 (9), 1003-1010 (2003). (Pubitemid 37078320)
6. S. L., Powell, W. B., Cohen, Z., Yang, J. D., Pierce, and M., Alberti, " Quantification of impervious surface in the Snohomish Water Resources Inventory Area of Western Washington from 1972-2006, ", Remote Sens. Environ., 112 (4), 1895-1908 (2008). 10.1016/j.rse.2007.09.010 (Pubitemid 351403657)
7. F., Yuan and M. E., Bauer, " Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery, ", Remote Sens. Environ., 106 (3), 375-386 (2007). 10.1016/j.rse.2006.09.003 (Pubitemid 46123398)
8. R., Xiao, Z., Ouyang, H., Zheng, W., Li, E. W., Schienke, and X., Wang, " Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China, " J. Environ. Sci. (China), 19 (2), 250-256 (2007). 10.1021/es062024x (Pubitemid 46047850)
9. Q., Weng and D., Lu, " A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States, ", Int. J. Appl. Earth Obs. and Geoinf., 10 (1), 68-83 (2008). 10.1016/j.jag.2007.05.002
10. Y., Zhang, I. O. A., Odeh and C., Han, " Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis, ", Int. J. Appl. Earth Obs. and Geoinf., 11 (4), 256-264 (2009). 10.1016/j.jag.2009.03.001
11. J., Reilly, P., Maggio, and S., Karp, " A model to predict impervious surface for regional and municipal land use planning purposes, ", Environ. Impact Asses. Rev., 24 (3), 363-382 (2004). 10.1016/j.eiar.2003.10.022 (Pubitemid 38225342)
12. D., Lu and Q., Weng, " Use of impervious surface in urban land-use classification, ", Remote Sens. Environ., 102 (1), 146-160 (2006). 10.1016/j.rse.2006.02.010
13. E., Brabec, S., Schulte, and P. L., Richards, " Impervious surface and water quality: a review of current literature and its implications for watershed planning, ", J. Plann. Lit., 16 (4), 499-514 (2002). 10.1177/088541202400903563 (Pubitemid 34500267)
14. E. A., Pappas, D. R., Smith, C., Huang, W. D., Shuster, and J. V., Bonta, " Impervious surface impacts to runoff and sediment discharge under laboratory rainfall simulation, ", Catena, 72 (1), 146-152 (2008). 10.1016/j.catena.2007.05.001 (Pubitemid 47600510)
15. C., Wu and A. T., Murray, " Estimating impervious surface distribution by spectral mixture analysis, ", Remote Sens. Environ., 84 (4), 493-505 (2003). 10.1016/S0034-4257(02)00136-0 (Pubitemid 36360511)
16. T., Esch, V., Himmler, G., Schorcht, M., Thiel, T., Wehrmann, F., Bachofer, C., Conrad, M., Schmidt, and S., Dech, " Large-area assessment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data, " Remote Sens. Environ., 113 (8), 1678-1690 (2009). 10.1016/j.rse.2009.03.012
17. Q., Weng and X., Hu, " Medium spatial resolution satellite imagery for estimating and mapping urban impervious surfaces using LSMA and ANN, ", IEEE Trans. Geosci. Remote Sens., 46 (8), 2397-2406 (2008). 10.1109/TGRS.2008.917601
18. Y., Zhang and B., Guindon, " Multi-resolution intergration of land cover for sub-pixel estimation of urban impervious surface and forest cover, ", Int. J. Digital Earth., 2 (2), 89-108 (2009). 10.1080/17538940802585507
19. L., Yang, C., Huang, C. G., Homer, B. K., Wylie, and M. J., Coan, " An approach for mapping large-scale impervious surfaces: synergistic use of Landsat-7 ETM+ and high spatial resolution imagery, ", Can. J. Remote Sensing, 29 (2), 230-240 (2003).
20. Q., Weng, X., Hu, and D., Lu, " Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison, ", Int. J. Remote Sens., 29 (11), 3209-3232 (2008). 10.1080/ 01431160701469024 (Pubitemid 351911288)
21. S. J., Goetz, R. K., Wright, A. J., Smith, E., Zinecker, and E., Schaub, " IKONOS imagery for resource management: Tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region, ", Remote Sens. Environ., 88 (1), 195-208 (2003). 10.1016/j.rse.2003.07.010 (Pubitemid 38184696)
22. D., Lu and Q., Weng, " Extraction of urban impervious surfaces from an IKONOS image, ", Int. J. Remote Sens., 30 (5), 1297-1311 (2009). 10.1080/01431160802508985
23. L., Jiang, M., Liao, H., Lin, and L., Yang, " Synergistic use of optical and InSAR data for urban impervious surface mapping: a case study in Hong Kong, ", Int. J. Remote Sens., 30 (11), 2781-2796 (2009). 10.1080/01431160802555838
24. S., Lee and R. G., Lathrop, " Subpixel analysis of Landsat ETM+ using self-organizing map (SOM) neural networks for urban land cover characterization, ", IEEE Trans. Geosci. Remote Sens., 44 (6), 1642-1654 (2006). 10.1109/TGRS.2006.869984 (Pubitemid 43824509)
25. J., Han and M., Kamber, Data Mining Concepts and Techniques, China Machine Press, Beijing (2007).
26. P. M., Atkinson and A. R. L, Tatnall, " Introduction neural networks in remote sensing, ", Int. J. Remote Sens., 18 (4), 699-709 (1997). 10.1080/014311697218700 (Pubitemid 27269964)
27. T., Kavzoglu and P. M., Mather, " The use of backpropagating artificial neural networks in land cover classification, ", Int. J. Remote Sens., 24 (23), 4907-4938 (2003). 10.1080/0143116031000114851 (Pubitemid 37519427)
28. C. J. C., Burges, " A tutorial on support vector machines for pattern recognition, ", Data Min. Knowl. Discov., 2 (2), 121-167 (1998). 10.1023/A:1009715923555 (Pubitemid 128126769)
29. W. -H., Chen, S.-H., Hsu, and H.-P., Shen, " Application of SVM and ANN for intrusion detection, ", Comput. Oper. Res., 32 (10), 2617-2634 (2005). 10.1016/j.cor.2004.03.019 (Pubitemid 40219764)
30. C., Huang, L. S., Davis, and J. R. G., Townshend, " An assessment of support vector machines for land cover classification, ", Int. J. Remote Sens., 23 (4), 725-749 (2002). 10.1080/01431160110040323 (Pubitemid 34119904)
31. V. N., Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York (1995).
32. G., Zhu, and D. G., Blumberg, " Classification using ASTER data and SVM algorithms: The case study of Beer Sheva, Israel, ", Remote Sens. Environ., 80 (2), 233-240 (2002). 10.1016/S0034-4257(01)00305-4 (Pubitemid 34409346)
33. G. M., Foody and A., Mathur, " A relative evaluation of multiclass image classification by support vector machines, ", IEEE Trans. Geosci. Remote Sens., 42 (6), 1335-1343 (2004). 10.1109/TGRS.2004.827257
34. M., Pal and P. M., Mather, " Support vector machines for classification in remote sensing, ", Int. J. Remote Sens., 26 (5), 1007-1011 (2005). 10.1080/01431160512331314083 (Pubitemid 40231241)
35. S., Ahmad, A., Kalra and H., Stephen, " Estimating soil moisture using remote sensing data: A machine learning approach, ", Adv. Water Resour, 33 (1), 69-80 (2010). 10.1016/j.advwatres.2009.10.008
36. W.-H., Wong and S.-H., Hsu, " Application of SVM and ANN for image retrieval, ", European J. Operat. Res., 173 (3), 938-950 (2006). 10.1016/j.ejor.2005.08.002 (Pubitemid 43866336)
37. T.-K., Wu, S.-C., Huang, and Y.-R., Meng, " Evaluation of ANN and SVM classifiers as predictors to the diagnosis of students with learning disabilities, ", Expert Syst. Applic., 34 (3), 1846-1856 (2008). 10.1016/j.eswa.2007.02.026 (Pubitemid 350298207)
38. G., Chander and B., Markham, " Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges, ", IEEE Trans. Geosci. Remote Sens., 41 (11), 2674-2677 (2003). 10.1109/TGRS.2003.818464
39. V. N., Vapnik, Statistical Learning Theory, Wiley, New York (1998).
40. O., Devos, C., Ruckebusch, A., Durand, L., Duponchel, and J.-P., Huvenne, " Support vector machines (SVM) in near infrared (NIR) spectroscopy: Focus on parameters optimization and model interpretation, ", Chemom. Intell. Lab. Syst., 96 (1), 27-33 (2009). 10.1016/j.chemolab.2008.11.005
41. C. C., Chang and C. J., Lin, LIBSVM:, A Library for Support Vector Machines. Department of Computer Science and Information Engineering, National Taiwan University, Taiwan (2008). Available online at:, http://www.csie.ntu.edu. tw/∼cjlin/libsvm (accessed 15 October 2008).
42. H., Xu, " A study on information extraction of water body with the modified normalized difference water index (MNDWI), ", J. Remote Sens., 9 (5), 589-595 (2005).
43. A. I., Belousov, S. A., Verzakov, and J. V., Frese, " A flexible classification approach with optimal generalisation performance: support vector machines, ", Chemom. Intell. Lab. Syst., 64, 15-25 (2002). 10.1016/S0169-7439(02)00046-1 (Pubitemid 35304556)