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

Authors: Ebrahim Taherzadeh, Helmi Z. M. Shafri, Kaveh Shahi

Abstract:

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% accuracy.

Keywords: Urban remote sensing, impervious surface, Object- Based, Roof Material, Concrete tile, WorldView-2.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096451

References:

[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. http://www.mapscapital.com/schoolpowers/ media/pdf/RoofSurfaceExtraction.pdf
[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, 2007.
[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, 2014.