A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery

Geocarto International

Volumn 29, Issue 4, 2014, Pages 351-369

  Tehrany, Mahyat Shafapour ^a   Pradhan, Biswajeet ^a   Jebuv, Mustafa Neamah ^a  

^a UNIVERSITY PUTRA MALAYSIA   (Malaysia)

Author keywords

decision tree; GIS; Image classification; Malaysia; object oriented; remote sensing; support vector machine

Indexed keywords

GIS; IMAGE ANALYSIS; IMAGE CLASSIFICATION; LAND COVER; LAND USE; MAPPING; NEAREST NEIGHBOR ANALYSIS; PIXEL; REMOTE SENSING; SATELLITE IMAGERY; SPOT; WORLD WIDE WEB;

EID: 84903538782     PISSN: 10106049     EISSN: None     Source Type: Journal    
DOI: 10.1080/10106049.2013.768300     Document Type: Article

References (63)

1. AlFugara AM, Pradhan B, Mohamed TA. 2009. Improvement of land-use classification using object-oriented and fuzzy logic approach. Applied Geomatics. 1(4):111-120 doi: 10.1007/s12518-009-0011-3.
2. Ashcroft, E., 2012. Using remotely sensed data and decision tree classifiers to determine if the changes in Accra, Ghana are concentrated in the most vulnerable areas. Doctoral dissertation, The George Washington University, USA.
3. Baatz, M. and Schäpe, A., 1999. Object-oriented and multi-scale image analysis in semantic networks. Proceedings of the 2nd international symposium on operationalization of remote sensing, 16-20 August 1999. Enschede: ITC.
4. Baatz M, Schäpe A. 2000. Multiresolution segmentation - an optimization approach for high quality multi-scale image segmentation. In: Strobl J, Blaschke T, Griesebner G, editors. Angewandte Geographische Informationsverarbeitung XII. Heidelberg: Wichmann-Verlag; p. 12-23.
5. Bhaskaran S, Paramananda S, Ramnarayan M. 2010. Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data. Applied Geography. 30(4):650-665.
6. Biro K, Pradhan B, Buchroithner M, Makeschin F. 2010. Use of Multi-temporal Satellite Data for Land-Use/Land-Cover Change Analyses and its Impacts on Soil Properties in the Northern Part of Gadarif Region, In: Reuter R, editor. Remote sensing for science, education, and natural and cultural heritage. Paris: EARSel 2010; p. 305-312.
7. Biro K, Pradhan B, Buchroithner M, Makeschin F. 2013. Land use/land cover change analysis and its impact on soil properties in the Northern Part of Gadarif Region, Sudan. Land Deg Dev. 24(1): 90-102, doi: 10.1002/ldr.1116.
8. Biro K, Pradhan B, Sulieman H, and Buchroithner M. 2012. Exploitation of TerraSAR-X data for land use/land cover analysis using object-oriented classification approach in the African Sahel area, Sudan. Journal of the Indian Society of Remote Sensing., doi: 10.1007/s12524-012-0230-7.
9. Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing. 65(1):2-16.
10. Boggs G. 2010. Assessment of SPOT 5 and QuickBird remotely sensed imagery for mapping tree cover in savannas. International Journal of Applied Earth Observation and Geoinformation. 12(4):217-224.
11. Bui DT, Pradhan B, Lofman O, and Revhaug I. 2012. Landslide susceptibility assessment in Vietnam using support vector machines, decision tree and Naïve Bayes models. Mathematical Problems in Engineering, doi:10.1155/2012/974638.
12. Cao X, Chen J, Imura H, Higashi O. 2009. A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data. Remote Sensing of Environment. 113(10):2205-2209.
13. Castilla G, Hay GJ. 2008. Image objects and geographic objects. In: Blaschke T, Lang S, Hay GJ, editors. Object-based image analysis spatial concepts for knowledge-driven remote sensing applications. Berlin: Springer-Verlag; p. 91-110.
14. Chi M, Feng R, Bruzzone L. 2008. Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem. Advances in space research. 41(11):1793-1799.
15. Congalton RG, Green K. 2009. Assessing the accuracy of remotely sensed data: principles and practices. 2nd ed. Boca Raton, FL: CRC Press.
16. Davranche A, Lefebvre G, Poulin B. 2010. Wetland monitoring using classification trees and SPOT-5 seasonal time series. Remote sensing of environment. 114(3):552-562.
17. Desclee B, Bogaert P, Defourny P. 2006. Forest change detection by statistical object-based method. Remote Sensing of Environment. 102(1):1-11.
18. Durieux L, Lagabrielle E, Nelson A. 2008. A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data. ISPRS Journal of Photogrammetry and Remote Sensing. 63(4):399-408.
19. Duro DC, Franklin SE, Dubé MG. 2012. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote sensing of environment. 118:259-272.
20. Foody GM, Mathur A. 2004. Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment. 93(1):107-117.
21. Foody GM. 2002. Status of land cover classification accuracy assessment. Remote sensing of environment. 80(1):185-201.
22. Foody GM, Mathur A. 2006. The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM. Remote Sensing of Environment. 103(2):179-189.
23. Gamanya R, De Maeyer P, De Dapper M. 2009. Object-oriented change detection for the city of Harare. Zimbabwe. Expert Systems with Applications. 36(1):571-588.
24. Gao Y, Kerle N, Mas JF. 2009. Object-based image analysis for coal fire-related land cover mapping in coal mining areas. Geocarto International. 24(1):25-36.
25. Gao Y, Marpu P, Niemeyer I, Runfola DM, Giner NM, Hamill T, Pontius RG Jr. 2011. Object-based classification with features extracted by a semi-automatic feature extraction algorithm-SEaTH. Geocarto International. 26(3):211-226.
26. Gatrell JD, Jensen RR. 2008. Sociospatial applications of remote sensing in urban environments. Geography Compass. 2(3):728-743.
27. Hu X, Weng Q. 2011. Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method. Geocarto International. 26(1):3-20.
28. Huang, L. and Ni, L., 2008. Object-Oriented Classification of High Resolution Satellite Image for Better Accuracy. In Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. ISARA- International Spatial Accurcy Research Association, Shanghai, 211-218.
29. Kennedy RE, Townsend PA, Gross JE, Cohen WB, Bolstad P, Wang YQ, Adams P. 2009. Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sensing of Environment. 113(7):1382-1396.
30. Leinenkugel P, Esch T, Kuenzer C. 2011. Settlement detection and impervious surface estimation in the Mekong Delta using optical and SAR remote sensing data. Remote sensing of environment. 115(12):3007-3019.
31. Lu D, Weng Q. 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing. 28(5):823-870.
32. Magee, K.S., 2011. Segmentation, object-oriented applications for remote sensing land cover and land use classification. Doctoral dissertation, University of Cincinnati, USA.
33. Mahmoud A, Elbialy S, Pradhan B, Buchroithner M. 2011. Field-based landcover classification using TerraSAR-X texture analysis. Advances in Space Research. 48(5):799-805.
34. Mandalia, H. M. and Salvucci, M. D. D., 2005. Using support vector machines for lane-change detection. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting. SAGE Publications, 49(22), 1965-1969.
35. Matinfar H, Sarmadian F, Alavi Panah SK, Heck RJ. 2007. Comparisons of object-oriented and pixel-based classification of land use/land cover types based on Lansadsat7, Etm+ spectral bands (case study: arid region of Iran). American-Eurasian J. Agric. & Environ. Sci. 2(4):448-456.
36. Mountrakis G, Im J, Ogole C. 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing. 66(3):247-259.
37. 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 sensing of environment. 115(5):1145-1161.
38. Nemmour H, Chibani Y. 2006. Multiple support vector machines for land cover change detection: An application for mapping urban extensions. ISPRS journal of photogrammetry and remote sensing. 61(2):125-133.
39. Nordkvist K, Granholm AH, Holmgren J, Olsson H, Nilsson M. 2012. Combining optical satellite data and airborne laser scanner data for vegetation classification. Remote Sensing Letters. 3(5):393-401.
40. Otukei JR, Blaschke T. 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation. 12:27-31.
41. Ouyang ZT, Zhang MQ, Xie X, Shen Q, Guo HQ, Zhao B. 2011. A comparison of pixel-based and object-oriented approaches to VHR imagery for mapping saltmarsh plants. Ecological Informatics. 6(2):136-146.
42. Pal M, Mather PM. 2003. An assessment of the effectiveness of decision tree methods for land cover classification. Remote sensing of environment. 86(4):554-565.
43. Perumal K, Bhaskaran R. 2010. Supervised classification performance of multispectral images. Journal of Computing. 2(2):124-129.
44. Petropoulos GP, Kalaitzidis C, Prasad Vadrevu K. 2012. Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery. Computers & Geosciences. 41:99-107.
45. Platt RV, Rapoza L. 2008. An evaluation of an object-oriented paradigm for land use/land cover classification. The Professional Geographer. 60(1):87-100.
46. Pradhan, B., 2013. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comp Geosci. 51: 350-365, doi: 10.1016/j.cageo.2012.08.023.
47. Pradhan B, Suleiman Z. 2009. Landcover mapping and spectral analysis using multi-sensor satellite data: a case study in Tioman Island. Malaysia. Journal of Geomatics. 3(2):71-78.
48. Ren J. 2011. ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging. Knowledge-Based Systems. 26:144-153.
49. Robles Granda PD. 2011. A new image classification algorithm based on additive groves. Unpublished MSc Thesis. Carbondale (IL): Southern Illinois University at Carbondale.
50. Shafri M, Zulhaidi H, Ramle FSH. 2009. A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island. Information Technology Journal. 8(1):64-70.
51. Su W, Zhang C, Zhu X, Li D. 2009. A hierarchical object oriented method for land cover classification of SPOT 5 imagery. WSEAS Transactions on Information Science and Applications. 6(3):437-446.
52. Tooke TR, Coops NC, Goodwin NR, Voogt JA. 2009. Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications. Remote Sensing of Environment. 113(2):398-407.
53. Vapnik VN. 1999. An overview of statistical learning theory. IEEE Transaction on Neural Networks. 10(5):988-999.
54. Vapnik VN, Chervonenkis AY. 1971. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability & Its Applications. 16(2):264-280.
55. Vaudour E, Carey V, Gilliot J. 2010. Digital zoning of South African viticultural terroirs using bootstrapped decision trees on morphometric data and multitemporal SPOT images. Remote sensing of environment. 114(12):2940-2950.
56. Versluis A, Rogan J. 2010. Mapping land-cover change in a Haitian watershed using a combined spectral mixture analysis and classification tree procedure. Geocarto International. 25(2):85-103.
57. Wang, P.F., Feng, X., Zhao, S., Xiao, P., and Xu, C. 2007. Comparison of object-oriented with pixel-based classification techniques on urban classification using TM and IKONOS imagery. 15th International Conference on Geoinformatics. SPIE Proceedings, Nanjing, China.J7522-J7522.
58. Wei, S., Chao, Z., Jianyu, Y., Honggan, W., Minjie, C., Anzhi, Y., Yingna, Z., and Chongli, S. 2010. Knowledge-Based Object Oriented Land Cover Classification Using SPOT5 Imagery in Forest-Agriculture Ecotones. Sensor Letters, 8(1), 22-31.
59. Yan G, Mas JF, Maathuis BHP, Xiangmin Z, VanDijk PM. 2006. Comparison of pixel-based and object-oriented image classification approaches-A case study in a coal fire area, Wuda, Inner Mongolia China. International Journal of Remote Sensing. 27(18):4039-4055.
60. Yu Q, Gong P, Clinton N, Biging G, Kelly M, Shirokauer. 2006. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing. 72(7):799-811.
61. Zeilhofer P, Topanotti VP. 2008. GIS and ordination techniques for evaluation of environmental impacts in informal settlements: a case study from Cuiabá. Central Brazil. Applied Geography. 28(1):1-15.
62. Zhang L, Lin F, Zhang B. 2001. Support vector machine learning for image retrieval. Proc. IEEE Int. Conf. on Image Processing. 2:721-724.
63. Zhou W, Troy A. 2008. An object-oriented approach for analysing and characterizing urban landscape at the parcel level. International Journal of Remote Sensing. 29(11):3119-3135.