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Original Articles

Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery

, &
Pages 1389-1406 | Received 04 Nov 2015, Accepted 12 Jul 2016, Published online: 02 Aug 2016

References

  • AASHTO. 2009. Rough roads ahead, fix them now or pay for it later. Technical Report. Washington (DC): A Joint Product of American Association of State Highway and Transportation Officials, and TRIP-a National Transportation Research Group.
  • Abdelhamid N, Ayesh A, Thabtah F. 2014. Phishing detection based Associative Classification data mining. Expert Syst Appl. 41:5948–5959.10.1016/j.eswa.2014.03.019
  • Aggarwal M, Prasad A. 2013. Fusion of statistic, data mining and genetic algorithm for feature selection in intrusion detection. Int J Adv Res Comput Eng Technol. 2:1725–1731.
  • Akiwowo A, Eftekhari M. 2013. Feature-based detection using Bayesian data fusion. Int J Image Data Fusion. 4:308–323.10.1080/19479832.2013.824029
  • Alqurashi AF, Kumar L. 2013. Investigating the use of remote sensing and GIS techniques to detect land use and land cover change: a review. Adv in Remote Sens. 2:193–204. doi:10.4236/ars.2013.22022.
  • Alvarez SA. 2003. Chi-squared computation for association rules: preliminary results. Boston, MA: Boston College.
  • Andreou C, Karathanassi V, Kolokoussis P. 2011. Investigation of hyperspectral remote sensing for mapping asphalt road conditions. Int J Remote Sens. 32:6315–6333.10.1080/01431161.2010.508799
  • Baatz M, Schäpe A. 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. In: Strobl J, Blaschke T, editors. Angewandte Geographische Informationsverarbeitung XII. Heidelberg: Wichmann; p. 12–23.
  • Bacher U, Mayer H. 2005. Automatic road extraction from multispectral high resolution satellite images. In: Stilla U, Rottensteiner F, Hinz S, editors. CMRT05, Vol. XXXVI, Part 3/W24. Vienna: IAPRS.
  • Ben-Dor E, Levin N, Saaroni H. 2001. A spectral based recognition of the urban environment using the visible and near-infrared spectral region (0.4-1.1 μm). A case study over Tel-Aviv, Israel. Int J Remote Sens. 22:2193–2218.
  • Bhaskaran S, Nez E, Jimenez K, Bhatia SK. 2013. Rule-based classification of high-resolution imagery over urban areas in New York City. Geocarto Int. 28:527–545.10.1080/10106049.2012.726278
  • Bhaskaran S, Paramananda S, Ramnarayan M. 2010. Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data. Appl Geogr. 30:650–665.10.1016/j.apgeog.2010.01.009
  • Bhatti A, Rundquist D, Schalles J, Ramirez L. 2010. Application of hyperspectral remotely sensed data for water quality monitoring: accuracy and limitation. In: Proceedings of the Proceedings of the accuracy symposium; Leicester, UK.
  • Blanco-Vogt Á, Haala N, Schanze J. 2015. Building parameters extraction from remote-sensing data and GIS analysis for the derivation of a building taxonomy of settlements – a contribution to flood building susceptibility assessment. Int J Image Data Fusion. 6:22–41.10.1080/19479832.2014.926296
  • Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS J Photogram Remote Sens. 65:2–16.10.1016/j.isprsjprs.2009.06.004
  • Breiman L. 2001. Random forests. Mach Learn. 45:5–32.10.1023/A:1010933404324
  • Cablk M, Minor T. 2003. Detecting and discriminating impervious cover with high-resolution IKONOS data using principal component analysis and morphological operators. Int J Remote Sens. 24:4627–4645.10.1080/0143116031000102539
  • Chapman DS, Bonn A, Kunin WE, Cornell SJ. 2010. Random forest characterization of upland vegetation and management burning from aerial imagery. J Biogeogr. 37:37–46.
  • Congalton RG. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ. 37:35–46.10.1016/0034-4257(91)90048-B
  • DigitalGlobe. 2009. White paper: the benefits of the 8 spectral bands of WorldView–2. Longmont, CO: DigitalGlobe
  • Elsharkawy A, Elhabiby M, El-Sheimy N. 2012. New combined pixel/object-based technique for efficient urban classification using WorldView-2 data. Proc Int Arch Photogram Remote Sens Spatial Inform Sci. 39:191–195.
  • ENVI-Zoom. 2010. ENVI user guide. Denver, CO: ITT.
  • Foody GM. 2004. Thematic map comparison. Photogram Eng Remote Sens. 70:627–633.
  • Ghimire B, Rogan J, Miller J. 2010. Contextual land-cover classification: incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic. Remote Sens Lett. 1:45–54.10.1080/01431160903252327
  • Greenacre M. 2008. Measures of distance between samples: Euclidean. Available form: http://www econ upf edu/~michael/stanford/maeb4 pdf.
  • Guyon I, Weston J, Barnhill S, Vapnik V. 2002. Gene selection for cancer classification using support vector machines. Mach Learn. 46:389–422.10.1023/A:1012487302797
  • Hamedianfar A, Shafri HZM. 2013. Development of fuzzy rule-based parameters for urban object-oriented classification using very high resolution imagery. Geocarto Int. 29:268–292.
  • Hamedianfar A, Shafri HZM. 2015. Detailed intra-urban mapping through transferable OBIA rule sets using WorldView-2 very-high-resolution satellite images. Int J Remote Sens. 36:3380–3396.10.1080/01431161.2015.1060645
  • Hamedianfar A, Shafri HZM. 2016. Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data. J Appl Remote Sens. 10:025001–025001.10.1117/1.JRS.10.025001
  • Hamedianfar A, Shafri HZM, Mansor S, Ahmad N. 2014a. Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images. J Appl Remote Sens. 8:085091–085091.10.1117/1.JRS.8.085091
  • Hamedianfar A, Shafri HZM, Mansor S, Ahmad N. 2014b. Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data. Int J Remote Sens. 35:1876–1899.10.1080/01431161.2013.879350
  • Heiden U, Roessner S, Segl K, Kaufmann H. 2001. Potential of hyperspectral HyMap data for material oriented identification of urban surfaces. Remote Sens Urban Areas. 69–77.
  • Herold M, Roberts D. 2005. Spectral characteristics of asphalt road aging and deterioration: implications for remote-sensing applications. Appl Opt. 44:4327–4334.10.1364/AO.44.004327
  • Herold M, Roberts DA, Gardner ME, Dennison PE. 2004. Spectrometry for urban area remote sensing – development and analysis of a spectral library from 350 to 2400 nm. Remote Sens Environ. 91:304–319.10.1016/j.rse.2004.02.013
  • Hu H, Liu Y, Wang X, Zhu X-j, Xu B. 2008. Road extraction in remote sensing images using a new algorithm. Intelligent info Hiding and Multimedia Signal Processing. IIHMSP'08 International Conference. Harbin: IEEE; p. 779–782.
  • Hu X, Weng Q. 2011. Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method. Geocarto Int. 26:3–20.10.1080/10106049.2010.535616
  • Huang M-L, Hung Y-H, Lee W, Li R, Jiang B-R. 2014. SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier. Sci World J. 2014:1–10.
  • Ji X, Niu X. 2014. The attribute accuracy assessment of land cover data in the national geographic conditions survey. ISPRS Ann Photogram Remote Sens Spatial Inform Sci. 2:35–40.
  • Jiang X, Tang L, Wang C, Wang C. 2004. Spectral characteristics and feature selection of hyperspectral remote sensing data. Int J Remote Sens. 25:51–59.10.1080/0143116031000115292
  • Jin C, Ma T, Hou R, Tang M, Tian Y, Al-Dhelaan A, Al-Rodhaan M. 2015. Chi-square statistics feature selection based on term frequency and distribution for text categorization. IETE J Res. 61:351–362.10.1080/03772063.2015.1086703
  • Jin X, Paswaters S. 2007. A fuzzy rule base system for object-based feature extraction and classification. Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 65671H. Florida, USA; doi:10.1117/12.720063.
  • Kavzoglu T. 2001. An investigation of the design and use of feed forward artificial neural networks in the classification of remotely sensed images [PhD thesis]. Nottingham: School of Geography, The University of Nottingham.
  • Kavzoglu T, Sen YE, Cetin M. 2009. Mapping urban road infrastructure using remotely sensed images. Int J Remote Sens. 30:1759–1769.10.1080/01431160802639582
  • Kirthika A, Mookambiga A. 2011. Automated road network extraction using artificial neural network. In: Proceedings of the Recent Trends in Information Technology (ICRTIT), 2011 International Conference on. Chennai: IEEE; p. 1061–1065.
  • Li X, Qiao Y, Yi W, Guo Z. 2003. The research of road extraction for high resolution satellite image. In: Proceedings of the Geoscience and Remote Sensing Symposium, 2003 IGARSS’03 Proceedings 2003 IEEE International. Beijing: IEEE; p. 3949–3951.
  • Lian L, Chen J. 2011. Research on segmentation scale of multi-resources remote sensing data based on object-oriented. Proc Earth Planet Sci. 2:352–357.10.1016/j.proeps.2011.09.055
  • Lin P. 2009. A framework for consistency based feature selection [masters theses]; 62.
  • Liu D, Xia F. 2010. Assessing object-based classification: advantages and limitations. Remote Sens Lett. 1:187–194.10.1080/01431161003743173
  • Lu D, Weng Q. 2009. Extraction of urban impervious surfaces from an IKONOS image. Int J Remote Sens. 30:1297–1311.10.1080/01431160802508985
  • Miller JS, Bellinger WY. 2014. Distress identification manual for the long-term pavement maintenance program. Report No. FHWA-RD-0-031. Washington (DC): Federal Highway Administration.
  • Mohammadi M. 2011. Road classification and condition determination using hyperspectral imagery [ Unpublished Master thesis]. Germany: University of Applied Sciences Stuttgart.
  • Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q. 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens Environ. 115:1145–1161.10.1016/j.rse.2010.12.017
  • Nguyen MH, de la Torre F. 2010. Optimal feature selection for support vector machines. Pattern Recogn. 43:584–591.10.1016/j.patcog.2009.09.003
  • Noronha V, Herold M, Roberts D, Gardner M. 2002. Spectrometry and hyperspectral remote sensing for road centerline extraction and evaluation of pavement condition. In: Proceedings of the Pecora Conference, Denver, CO, USA.
  • Novakovic J. 2009. Using information gain attribute evaluation to classify sonar targets. In: Proceedings of the 17th Telecommunications forum TELFOR. Belgrade; p. 1351–1354.
  • Pal M. 2005. Random forest classifier for remote sensing classification. Int J Remote Sens. 26:217–222.10.1080/01431160412331269698
  • Pal M. 2006. Support vector machine-based feature selection for land cover classification: a case study with DAIS hyperspectral data. Int J Remote Sens. 27:2877–2894.10.1080/01431160500242515
  • Prospere K, McLaren K, Wilson B. 2014. Plant species discrimination in a tropical wetland using in situ hyperspectral data. Remote Sens. 6:8494–8523.10.3390/rs6098494
  • Raghavendra B, Simha JB. 2010. Evaluation of feature selection methods for predictive modeling using neural networks in credits scoring. Int J Adv Network Appl. 2:714–718.
  • Resende M, Jorge S, Longhitano G, Quintanilha JA. 2008. Use of hyperspectral and high spatial resolution image data in an asphalted urban road extraction. In: Proceedings of the Geoscience and Remote Sensing Symposium, 2008 IGARSS 2008 IEEE International. Boston, MA: IEEE; p. III-1323–III-1325.
  • Roberts DA, Herold M. 2004. Imaging spectrometry of urban materials. In: King, PL, Ramsey, MS, Swayze, G, editors. Infrared Spectroscopy in Geochemistry, Exploration and Remote Sensing. London: Mineral Association of Canada, Short Course Series Volume 33; p. 155–181.
  • Saeys Y, Inza I, Larranaga P. 2007. A review of feature selection techniques in bioinformatics. Bioinformatics. 23:2507–2517.10.1093/bioinformatics/btm344
  • Samsudin SH, Shafri HZM, Hamedianfar A, Mansor S. 2015. Spectral feature selection and classification of roofing materials using field spectroscopy data. J Appl Remote Sens. 9:095079–095079.10.1117/1.JRS.9.095079
  • Santos A, Celes CdS, Araújo AdA, Menotti D. 2012. Feature selection for classification of remote sensed hyperspectral images: a filter approach using genetic algorithm and cluster validity. In: Proceedings of The 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV’12), Athens.
  • Schnebele E, Tanyu BF, Cervone G, Waters N. 2015. Review of remote sensing methodologies for pavement management and assessment. Eur Trans Res Rev. 7:1–19.
  • Shafri HZM, Hamedianfar A. 2015. Mapping of intra-urban land covers using pixel-based and object-based classifications from airborne hyperspectral imagery. In: Proceedings of the Information Science and Security (ICISS), 2015 2nd International Conference on. Seoul: IEEE; p. 1–4.
  • Shafri HZM, Taherzadeh E, Mansor S, Ashurov R. 2012. Hyperspectral remote sensing of urban areas: an overview of techniques and applications. Res J Appl Sci Eng Technol. 4:1557–1565.
  • Taherzadeh E, Shafri HZM. 2013. Development of a generic model for the detection of roof materials based on an object-based approach using WorldView-2 satellite imagery. Adv Remote Sens. 2:312–321.10.4236/ars.2013.24034
  • Taşcı Ş, Güngör T. 2013. Comparison of text feature selection policies and using an adaptive framework. Expert Syst Appl. 40:4871–4886.
  • Taubenböck H, Esch T, Wurm M, Roth A, Dech S. 2010. Object-based feature extraction using high spatial resolution satellite data of urban areas. J Spat Sci. 55:117–132.10.1080/14498596.2010.487854
  • Tian J, Chen DM. 2007. Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition. Int J Remote Sens. 28:4625–4644.10.1080/01431160701241746
  • Tiong PLY, Mustaffar M, Hainin MR. 2012. Road surface assessment of pothole severity by close range digital photogrammetry method. World Appl Sci J. 19:867–873.
  • Wang Y, Li X, Zhang L, Zhang W. 2008. Automatic road extraction of urban area from high spatial resolution remotely sensed imagery. Int Arch Photogram Remote Sens Spatial Inform Sci. 86:59–62.
  • Weng Q. 2012. Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends. Remote Sens Environ. 117:34–49.10.1016/j.rse.2011.02.030
  • Witten IH, Frank E. 1999. Data mining: practical machine learning tools and techniques with Java implementations. San Francisco, CA: Morgan Kaufmann.
  • Wu C. 2009. Quantifying high-resolution impervious surfaces using spectral mixture analysis. Int J Remote Sens. 30:2915–2932.10.1080/01431160802558634
  • Yang H, Ke-ju Z. 2009. Road extraction from remote sensing imagery based on road tracking and ribbon snake. In: Proceedings of the Knowledge Engineering and Software Engineering, 2009 KESE’09 Pacific-Asia Conference on. Shenzhen: IEEE, p. 201–204.
  • Zahidi I, Yusuf B, Hamedianfar A, Shafri HZM, Mohamed TA. 2015. Object-based classification of QuickBird image and low point density LIDAR for tropical trees and shrubs mapping. Eur J Remote Sens. 48:423–446.10.5721/EuJRS
  • Zhang Y. 2002. A new automatic approach for effectively fusing Landsat 7 as well as IKONOS images. In: Proceedings of the IEEE/IGARSS. Toronto, Canada; p. 2429–2431.
  • Zhang Q, Couloigner I. 2004. A framework for road change detection and map updating. Int Arch Photogram Remote Sens Spatial Inform Sci. 35:720–734.

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