Volume 8, Issue 4 (2020)
ECOPERSIA 2020, 8(4): 209-219 |
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Behnia N, Zare M, Moosavi V, Khajeddin S. Evaluation of a Hierarchical Classification Method and Statistical Comparison with Pixel-Based and Object-Oriented Approaches. ECOPERSIA 2020; 8 (4) :209-219
URL: http://ecopersia.modares.ac.ir/article-24-38774-en.html
URL: http://ecopersia.modares.ac.ir/article-24-38774-en.html
1- Department of Arid Lands Management, Faculty of Natural Resources, Yazd University, Yazd, Iran
2- Department of Arid Lands Management, Faculty of Natural Resources, Yazd University, Yazd, Iran ,mzernani@yazd.ac.ir
3- Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Tehran, Iran
4- Department of Range and Watershed Management, Faculty of Natural Resources, Isfahan University of Technology, Isfahan, Iran
2- Department of Arid Lands Management, Faculty of Natural Resources, Yazd University, Yazd, Iran ,
3- Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Tehran, Iran
4- Department of Range and Watershed Management, Faculty of Natural Resources, Isfahan University of Technology, Isfahan, Iran
Abstract: (2209 Views)
Aims: Producing a land use/land cover map is a fundamental step in different studies. This study aimed to assess the ability of hierarchical, pixel-based and object-oriented classification methods to produce land use/cover maps.
Materials & Methods: This study was conducted in the Harat-Marvast basin of Yazd Province, Iran using Landsat imagery of 2016 (paths 161 and 162, row 39). The hierarchical image classification method was tested for land use/cover mapping. A statistical comparison between three algorithms, namely pixel-based, object-oriented and hierarchical image classification was performed using the McNemar test. An intensive field survey was also accomplished to obtain training and test samples.
Findings: The kappa coefficients for pixel-based, hierarchical and object-oriented techniques were 0.76, 0.83 and 0.94, respectively. Results also showed that the performance of SVM and hierarchical algorithms are significantly different with aχ2f 112.3 which shows the superior performance of the hierarchical algorithm.
Conclusion: It was shown that the object-oriented approach performed significantly better than the two above-mentioned methods (χ2= 149.6). As the computational costs of object-oriented methods are relatively high, the hierarchical algorithm can be suggested when there are limitations in time or computational infrastructures. Therefore, the hierarchical algorithm can be used instead of simple pixel-based algorithms for land use/cover mapping.
Materials & Methods: This study was conducted in the Harat-Marvast basin of Yazd Province, Iran using Landsat imagery of 2016 (paths 161 and 162, row 39). The hierarchical image classification method was tested for land use/cover mapping. A statistical comparison between three algorithms, namely pixel-based, object-oriented and hierarchical image classification was performed using the McNemar test. An intensive field survey was also accomplished to obtain training and test samples.
Findings: The kappa coefficients for pixel-based, hierarchical and object-oriented techniques were 0.76, 0.83 and 0.94, respectively. Results also showed that the performance of SVM and hierarchical algorithms are significantly different with aχ2f 112.3 which shows the superior performance of the hierarchical algorithm.
Conclusion: It was shown that the object-oriented approach performed significantly better than the two above-mentioned methods (χ2= 149.6). As the computational costs of object-oriented methods are relatively high, the hierarchical algorithm can be suggested when there are limitations in time or computational infrastructures. Therefore, the hierarchical algorithm can be used instead of simple pixel-based algorithms for land use/cover mapping.
Article Type: Original Research |
Subject:
Desert Ecosystems
Received: 2019/12/6 | Accepted: 2020/03/6 | Published: 2020/09/22
Received: 2019/12/6 | Accepted: 2020/03/6 | Published: 2020/09/22
References
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1- Chen W, Xu Q, Zhao K, Zhou X, Li S, Wang J, et al. Spatial analysis of land-use management for gully land consolidation on the Loess Plateau in China. Ecol Indic. 2020;117:106633. [Link] [DOI:10.1016/j.ecolind.2020.106633]
2. Elhag AMH, Abubaker Haroun MA, Almaleeh RE. Desertification Assessment, using Remote Sensing, GIS and other techniques. Case study: Wadi Al Kanger, Sudan. J Nat Resour Environ Study. 2014;(10):1-6. [Link]
3. Cetin M. A satellite based assessment of the impact of urban expansion around a lagoon. Int J Environ Sci Technol. 2009;6:579-90. [Link] [DOI:10.1007/BF03326098]
4. Zhang DD, Zhang L, Zaborovsky V, Xie F, Wu YW, Lu TT. Research on the pixel-based and object-oriented methods of urban feature extraction with GF-2 remote-sensing images. arXiv. 2019;1903.03412. [Link]
5. Berhane TM, Lane CR, Wu Q, Anenkhonov OA, Chepinoga VV, Autrey BC, et al. Comparing pixel-and object-based approaches in effectively classifying wetland-dominated landscapes. Remote Sens. 2018;10(1):46. [Link] [DOI:10.3390/rs10010046]
6. Mohammady M, Moradi H, Zeinivand H, Temme AJAM. A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran. Int J Environ Sci Technol. 2015;12:1515-26. [Link] [DOI:10.1007/s13762-014-0728-3]
7. Meneguzzo DM, Liknes GC, Nelson MD. Mapping trees outside forests using high-resolution aerial imagery: A comparison of pixel-and object-based classification approaches. Environ Monit Assess. 2013;185:6261-75. [Link] [DOI:10.1007/s10661-012-3022-1]
8. Rahman MR, Saha SK. Multi-resolution segmentation for object-based classification and accuracy assessment of land use/land cover classification using remotely sensed data. J Indian Soc Remote Sens. 2008;36(2):189-201. [Link] [DOI:10.1007/s12524-008-0020-4]
9. Karami A, Khoorani A, Noohegar A, Shamsi SRF, Moosavi V. Gully erosion mapping using object-based and pixel-based image classification methods. Environ Eng Geosci. 2015;21(2):101-10. [Link] [DOI:10.2113/gseegeosci.21.2.101]
10. Rozenstein O, Karnieli A. Comparison of methods for land-use classification incorporating remote sensing and GIS inputs. Appl Geogr. 2011;31(2):533-44. [Link] [DOI:10.1016/j.apgeog.2010.11.006]
11. Jobin B, Labrecque S, Grenier M, Falardeau G. Object-based classification as an alternative approach to the traditional pixel-based classification to identify potential habitat of the Grasshopper Sparrow. Environ Manag. 2008;41:20-31. [Link] [DOI:10.1007/s00267-007-9031-0]
12. Hong X, Pradhan B, Xu Ch, Bui DT. Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. CATENA. 2015;133:266-81. [Link] [DOI:10.1016/j.catena.2015.05.019]
13. Moosavi V, Talebi A, Shirmohammadi B. Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method. Geomorphology. 2014;204:646-56. [Link] [DOI:10.1016/j.geomorph.2013.09.012]
14. Zoleikani R, Vahedan Zoej MJ, Mokhtarzadeh M. Comparison of pixel and object oriented based classification of hyperspectral pansharpened images. J Indian Soc Remote Sens. 2017;45:25-33. [Link] [DOI:10.1007/s12524-016-0573-6]
15. Ouyang ZT, Zhang MQ, Xie X, Shen Q, Guo HQ, Zhao B. A comparison of pixel-based and object-oriented approaches to VHR imagery for mapping saltmarsh plants. Ecol Inform. 2011;6(2):136-46. [Link] [DOI:10.1016/j.ecoinf.2011.01.002]
16. Keyport RN, Oommen T, Martha TR, Sajinkumar KS, Gierke JS. A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images. Int J Appl Earth Observ Geoinf. 2018;64:1-11. [Link] [DOI:10.1016/j.jag.2017.08.015]
17. Fathizad H, Hakimzadeh Ardakani MA, Taghizadeh Mehrjardi R, Sodaiezadeh H. Evaluating desertification using remote sensing technique and object-oriented classification algorithm in the Iranian central desert. J Afr Earth Sci. 2018;145:115-30. [Link] [DOI:10.1016/j.jafrearsci.2018.04.012]
18. Khiali L, Ienco D, Teisseire M. Object-oriented satellite image time series analysis using a graph-based representation. Ecol Inform. 2018;43:52-64. [Link] [DOI:10.1016/j.ecoinf.2017.11.003]
19. Yan Z, Sheng CD, Zhong RH. The Research of Building Earthquake Damage Object-Oriented Segmentation Based on Multi Feature Combination with Remote Sensing Image. Procedia Comput Sci. 2019;154:817-23. [Link] [DOI:10.1016/j.procs.2019.06.077]
20. Rasuly A, Naghdifar R, Rasoli M. Monitoring of Caspian Sea coastline changes using object-oriented techniques. Procedia Environ Sci. 2010;2:416-26. [Link] [DOI:10.1016/j.proenv.2010.10.046]
21. Yu Q, Gong P, Clinton N, Biging G, Kelly M, Schirokauer D. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogramm Eng Remote Sens. 2006;72(7):799-811. [Link] [DOI:10.14358/PERS.72.7.799]
22. Hay GJ, Blaschke T, Marceau DJ, Bouchard A. A comparison of three image-object methods for the multiscale analysis of landscape structure. ISPRS J Photogramm Remote Sens. 2003;57(5-6):327-45. [Link] [DOI:10.1016/S0924-2716(02)00162-4]
23. Sothe C, Almeida CM, Liesenberg V, Schimalski MB. Evaluating sentinel-2 and Landsat-8 data to map sucessional forest stages in a subtropical forest in Southern Brazil. Remote Sens. 2017;9(8):838. [Link] [DOI:10.3390/rs9080838]
24. Norrisa J, Walker J. Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States. Remote Sens Environ. 2020;249:112013. [Link] [DOI:10.1016/j.rse.2020.112013]
25. Borowik T, Pettorelli N, Sönnichsen L, Jędrzejewska B. Normalized difference vegetation index (NDVI) as a predictor of forage availability for ungulates in forest and field habitats. Eur J Wildl Res. 2013;59:675-82. [Link] [DOI:10.1007/s10344-013-0720-0]
26. Zha Y, Gao J, Ni S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens. 2003;24(3):583-94. [Link] [DOI:10.1080/01431160304987]
27. As-Syakur AR, Sandi Adnyana IW, Arthana IW, Nuarsa IW. Enhanced built-up and bareness index (EBBI) for mapping built-up and bare land in an urban area. Remote Sens. 2012;4(10):2957-70. [Link] [DOI:10.3390/rs4102957]
28. Li N, Martin A, Estival R. Heterogeneous information fusion: Combination of multiple supervised and unsupervised classification methods based on belief functions. Inform Sci. 2021;544:238-65. [Link] [DOI:10.1016/j.ins.2020.07.039]
29. Foody GM. Fully fuzzy supervised classification of land cover from remotely sensed imagery with an artificial neural network. Neural Comput Application. 1997;5:238-47. [Link] [DOI:10.1007/BF01424229]
30. Singh A, Bhatia R, Singhrova A. Taxonomy of machine learning algorithms in software fault prediction using object oriented metrics. Procedia Comput Sci.2018;132:993-1001. [Link] [DOI:10.1016/j.procs.2018.05.115]
31. Wei C, Ke CB, Liang SB, Cao S, Ma HT, Zhang XP. An improved phase field method by using statistical learning theory-based optimization algorithm for simulation of martensitic transformation in NiTi alloy. Comput Mater Sci. 2020;172:109292. [Link] [DOI:10.1016/j.commatsci.2019.109292]
32. Moosavi V, Talebi A, Mokhtari MH, Hadian MR. Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches. Int J Remote Sens. 2016;37(23):5605-31. [Link] [DOI:10.1080/01431161.2016.1244366]
33. Petropoulos GP, Kalaitzidis C, Vadrevu KP. Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery. Comput GeoSci. 2012;41:99-107 [Link] [DOI:10.1016/j.cageo.2011.08.019]
34. Okwuashi O, Ndehedehe CE. Deep support vector machine for hyperspectral image classification. Pattern Recognit. 2020;103:107298. [Link] [DOI:10.1016/j.patcog.2020.107298]
35. Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, Lopez A. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing. 2020;408:189-215. [Link] [DOI:10.1016/j.neucom.2019.10.118]
36. Belousov AI, Verzakov SA, von Frese J. Applicational aspects of support vector machines. J Chemom. 2002;16(8‐10):482-9. [Link] [DOI:10.1002/cem.744]
37. Marjanović M, Kovačević M, Bajat B, Voženílek V. Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol. 2011;123(3):225-34. [Link] [DOI:10.1016/j.enggeo.2011.09.006]
38. Blaschke T. Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens. 2010;65(1):2-16. [Link] [DOI:10.1016/j.isprsjprs.2009.06.004]
39. Manandhar R, Odeh IOA, Ancev T. Improving the accuracy of land use and land cover classification of landsat data using post-classification enhancement. Remote Sens. 2009;1(3):330-44. [Link] [DOI:10.3390/rs1030330]
40. Brovelli MA, Molinari ME, Hussein E, Chen J, Li R. The first comprehensive accuracy assessment of GlobeLand30 at a national level: Methodology and results. Remote Sens. 2015;7(4):4191-212. [Link] [DOI:10.3390/rs70404191]
41. Weih RC, Riggan ND. Object-based classification vs. pixel-based classification: Comparative importance of multi-resolution imagery. Int Arch Photogramm Remote Sens Spat Inf Sci. 2010;XXXVIII-4/C7. [Link]
42. Foody GM. Thematic map comparison: Evaluating the statistical significance of differences in classification Accuracy. Photogramm Eng Remote Sens. 2004;70(5):627-33. [Link] [DOI:10.14358/PERS.70.5.627]
43. Caroline Voisin SA. Bioinformatic and biostatistic methods for DNA methylome analysis of obesity. In: Wei LK, editor. Computational epigenetics and diseases. Cambridge: Academic Press; 2019. pp. 165-79. [Link] [DOI:10.1016/B978-0-12-814513-5.00011-8]
44. Xu K. Asymptotically distribution-free statistical test for generalized lorenz curves: An alternative approach. J Income Distrib. 1997;7(1):45-62. [Link] [DOI:10.1016/S0926-6437(97)80004-2]
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