Roof Material Detection Based on Object-Based Approach Using WorldView-2 Satellite Imagery

One of the most important tasks in urban remote
sensing is the detection of impervious surfaces (IS), such as roofs and
roads. However, detection of IS in heterogeneous areas still remains
one of the most challenging tasks. In this study, detection of concrete
roof using an object-based approach was proposed. A new rule-based
classification was developed to detect concrete roof tile. This
proposed rule-based classification was applied to WorldView-2
image and results showed that the proposed rule has good potential to
predict concrete roof material from WorldView-2 images, with 85%

[1] E. Taherzadeh, and H. Z. M. Shafri. "Development of a Generic Model
for the Detection of Roof Materials Based on an Object-Based Approach
Using WorldView-2 Satellite Imagery." Adv. in Remote Sens 2013.
[2] C. L. J. Arnold, and C. J. Gibbons, "Impervious Surface Coverage: the
Emergence of a Key Environmental Indicator,” J. of Am Plan Assoc,
Vol. 62, No. 2, 1996, pp. 243-258.
[3] Z. Sun, H. Guo, X. Li, L. Lu and X. Du, "Estimating Urban Impervious
Surfaces from Landsat-5 TM Imagery Using Multilayer Perceptron
Neural Network and Support Vector Machine,” J. of App. Remote Sens,
Vol. 5, No. 1, 2011.
[4] T. R. Oke, "Boundary Layer Climates,2nd Edition, Methuen and Co.
Ltd., Routledge, New York, 1987.
[5] Y. Yang and P. Pan, "Research on the Impact of Impervious Surface
Area on Urban Heat Island in Jiangsu Province,” Proceeding of SPIE
8286, Int. Symposium on Lidar and Radar Mapping, Technologies and
Applications, Nanjing, 26 May 2011.
[6] L. Cao, P. Li and L. Zhang, "Impact of Impervious surface face on
Urban Heat Island in Wuhan, China,” Proceed-ings in Int. Conf. on
Earth Observation Data Proc. and Anal. (ICEODPA), Wuhan, 29
December 2008.
[7] I. J. A. Callejas, A. S. De Oliveira, FC. Durante and M. C. De J. A.
Nogueira, "Relationship between Land Use/Cover and Surface
Temperatures in the Urban Agglomeration of Cuiabá-Várzea Grande,
Central Brazil,” J. of Appl. Remote Sens., Vol. 5, No. 1, 2011.
[8] D. Lu, and Q. Weng. "Use of impervious surface in urban land-use
classification”. Remote Sens of Environ. 102(1): 146-160, 2006.
[9] 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 of Environ.
106(3): 375–386, 2007.
[10] Q. Weng, "Modeling Urban Growth Effect on Surface Runoff with the
integration of Remote sensing and GIS”. Environmental Management,
Vol. 28, No. 6, 2001, pp. 737-748.
[11] Y. Wang and X. Zhang, "A SPLIT Model for Extraction of Sub-Pixel
Impervious Surface Information” Photogramm. Eng. and Remote Sens,
Vol. 70, No. 7, 2004, pp. 821-828.
[12] S. E. Clark, K. A. Steele, J. Spicher, C. Y. S. Siu, M. M. Lalor, R. Pitt
and J. T. Kirby, " Roofing Materials’ contributions to Storm-Water
Runoff Pollution,” J. of Irrigation and Drainage Eng. Vol. 134, No. 5,
2008, pp. 638-645.
[13] S. Bhaskaran, B. Datt, T. Neal and B. Forster, "Hail Storm Vulnerability
Assessment by Using Hyperspectral Remote Sensing and GIS
Techniques,” Proc. of the IGARSS Symposium, Sydney, 9-13 July
2001, pp. 1826-1828.
[14] A. Szykier, "Extraction of Roof Surface for Solar Analysis,” Maps
Capital Management, 2008.
[15] U. Rajasekar and Q. Weng, "Urban Heat Island Monitoring and Analysis
Using Nonparametric Model: A Case of Indianapolis,” ISPRS J. of
Remote Sens, Vol. 64, No. 1, 2009, pp. 86-96.
[16] M. Herold, M. Gardner, B. Hadley and D. Roberts, "The Spectral
Dimension in Urban Land Cover Mapping from High-Resolution
Optical Remote Sensing Data,” Proceedings of the 3rd Symposium on
Remote Sen. of Urban Areas, Istanbul, June 2002.
[17] P. Wang, X. Feng, S. Zhao, P. Xiao and C. Xu, "Com-parison of Object-
Oriented with Pixel-Based Classification Techniques on Urban
Classification Using TM and IKONOS Imagery,” Proc. in SPIE 6752,
Geoinformatics, Nanjing, 26 July 2007.
[18] E. Taherzadeh, H. Z. M. Shafri, S. H. K. Soltani, M. Shattri and R.
Ashurov, "A Comparison between different Pixel-Based Classification
Methods Over Urban Area Using Very High Resolution Data,” ASPRS
Annual Conf., Sacramento, 19-23 March 2012.
[19] S. V. D. Linden, A. Janz, B. Waske, M. Eiden and P.Hostert,
"Classifying Segmented Hyperspectral Data from a Heterogeneous
Urban Environment Using Support Vector Machines,” J. of Appl.
Remote Sens. Vol. 1, No. 1, 2007.
[20] D. Chen, D. A. Stow and P. Gong, "Examining the Effect of Spatial
Resolution and Texture Window Size on classification Accuracy: An
Urban Environment Case,” Int. J. of Remote Sens. Vol. 25, No. 11,
2004, pp. 1-16.
[21] L. Wang, Q. Dai, L. Hong and G. Liu, "Adaptive Regional Feature
Extraction for Very High Spatial resolution Image Classification,” J. of
Applied Remote Sens. Vol. 6, No. 1, 2012.
[22] Wang, P., Feng, X., Zhao, S., Xiao, P., and Xu, C, "Comparison of
object-oriented with pixel-based classification techniques on urban
classification using TM and IKONOS imagery.” Proc. In SPIE 6752,
Geoinformatics: Remote Sens. Data and Info. Nanjing, China , May. 25,
[23] Gong, P., Marceau, D.J., and Howarth, P.J, "A comparison of spatial
feature extraction algorithms for land-use classification with SPOT HRV
data.” Remote Sens of Environ. 40(2): 137–151, 1992.
[24] Shackelford, A. K., and Davis, C.H, "A combined fuzzy pixel-based and
object-based approach for classification of high-resolution multispectral
data over urban areas.” IEEE Transa Geoscience and Remote Sens.
41(10): 2354–2363, 2003.
[25] Wang, L., Dai, Q., Hong, L., and Liu, G, "Adaptive regional feature
extraction for very high spatial resolution image classification.” J. of
App. Remote Sens. 6(1): 063506, 2012.
[26] Goetz, S.J., Wright, R.K., Smith, A.J., Zinecker, E., and Schaub, E.,
"IKONOS imagery for resource management: tree cover, impervious
surfaces and riparian buffer analyses in the mid-Atlantic region.” J. of
Remote Sens. of Environ. 88(1): 195–208, 2003.
[27] Lu, D., and Weng, Q., "A survey of image classification methods and
techniques for improving classification performance.” Int. J. of Remote
Sens. 28(5): 823–870, 2007.
[28] U. C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder and M. Heynen,
"Multiresolution, "Object-Oriented Fuzzy Analysis of Remote Sensing
Data for GIS-Ready Information,” ISPRS J. of Photogramm. and
Remote Sens. Vol. 58, No. 3-4, 2004, pp. 239-258.
[29] L. Wang, W. P. Sousa, P. Gong and G. S. Biging, "Comparison of
IKONOS and QuickBird Images for Mapping Mangrove Species on the
Caribbean Coast of Panama,” Remote Sens. of Environ. Vol. 91, No. 3-
4, 2004, pp. 432-440.
[30] Liu, D., and F. Xia. "Assessing Object-Based Classification: Advantages
and Limitations.” Remote Sens. Letters 1 (4): 187–194, 2010.
[31] A. Hamedianfar, H. Z. M. Shafri, S. Mansor, and N. Ahmad. "Improving
detailed rule-based feature extraction of urban areas from WorldView-2
image and lidar data." Int. J. of Remote Sens. 35, no. 5: 1876-1899,