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Comparison of different algorithms for land use mapping in dry climate using satellite images: a case study of the Central regions of Iran | ||
Desert | ||
مقاله 1، دوره 20، شماره 1، فروردین 2015، صفحه 1-10 اصل مقاله (444.85 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/jdesert.2015.54077 | ||
نویسندگان | ||
Saleh Yousefi1؛ Somayeh Mirzaee2؛ Mehdi Tazeh* 3؛ Hamidreza Pourghasemi1؛ Haji Karimi4 | ||
1Department of Watershed Management, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran | ||
2Department of Watershed Management, Faculty of Natural Resources, Lorestan University, Khoramabad, Iran | ||
3Faculty of Natural Resources, Ardekan University, Ardekan, Iran | ||
4Faculty of Natural Resources, Ilam University, Ilam, Iran | ||
چکیده | ||
The objective of this research was to determine the best model and compare performances in terms of producing land use maps from six supervised classification algorithms. As a result, different algorithms such as the minimum distance of mean (MDM), Mahalanobis distance (MD), maximum likelihood (ML), artificial neural network (ANN), spectral angle mapper (SAM), and support vector machine (SVM) were considered in three areas of Iran's dry climate. The selected study areas for dry climates were Shahreza, Taft and Zarand in Isfahan, Yazd, and Kerman Provinces, respectively. Three Landsat ETM+ images and topographical maps of 1:25,000-scale were used in the present study. In addition, training samples for each land use were constructed using GPS and extensive field surveys. The training sites were divided into two categories; one category was used for image classification and the other for classification accuracy assessment. Results show that for the dry climate areas, Maximum Likelihood and Support Vector Machine algorithms with averages of 0.9409 and 0.9315 Kappa coefficients are the best algorithms for land use mapping. The ANOVA test was performed on Kappa coefficients, and the result shows that there are significant differences at the 1% level, between the different algorithms for the dry climate zones. These results can be used for land use planning, as well as environmental and natural resources purposes in study areas. | ||
کلیدواژهها | ||
Arid regions؛ land cover؛ remote sensing؛ SVM | ||
مراجع | ||
Aguilar, M.A., M.M. Saldaña, F.J. Aguilar, 2012. GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments. International Journal of Remote Sensing, 34; 2583– 2606. Al-Ahmadi, F. S., A.S. Hames, 2009. Comparison of four classification methods to extract land use and land cover from raw satellite images for some remote arid areas, Kingdom of Saudi Arabia. JKAU. Earth Science, 20; 167-191. Bargiel, D., 2013. Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes. Ecological Informatics, 13; 9-16. Bishop, Y.M.M., S.E. Fienberg, W. Paul, Holland, 1975. Discrete Multivariate Analysis: Theory and Practice. MIT Press, Cambridge. Bovolo, F., L. Bruzzone, L. Carlin, 2010. A novel technique for sub-pixel image classification based on support vector machine. IEEE Transactions on Image Processing, 19; 2983 – 2999. Bray, M., D. Han, 2004. Identification of support vector machines for runoff modelling. Journal of Hydroinformatics, 6; 265–280. Brown, M., H. Lewis, S. Gunn, 2000. Linear spectral mixture models and support vector machines for remote sensing, IEEE Transactions on Geosciences and Remote Sensing, 38 (5); 2346–2360. Chu, T.H., L. Ge, A.H. Ng, C. Rizos, 2012. Application of Genetic Algorithm and Support Vector Machine in Classification of Multisource Remote Sensing Data. International Journal of Remote Sensing Applications, 2; 1-11. De Backer, A., S. Adam, J. Monbaliu, E. Toorman, M. Vincx, S. Degraer, 2009. Remote Sensing of Biologically Reworked Sediments: A Laboratory Experiment. Estuaries and Coasts. DOI 10.1007/s12237-009-9204-6. Demorate, F. 1998. Land cover mapping estimated in Rendonia, Brazil. Journal of Remote Sensing, 19; 17-29. Dixon, B., N. Candade, 2008. Multispectral land use classification using neural networks and support vector machines: one or the other, or both, International Journal of Remote Sensing 29; 1185–1206. Du, Y., C. Chang, H. Ren, C. Chang, J.O. Jensen, F.M. D’Amico, 2004. New hyperspectral discrimination measure for spectral characterization. Optical Engineering, 43; 1777- 1786. Duncan, D.B. 1995. Multiple range and multiple F tests. Biometrics, 11:1–42, 1955. Elizabeth, A.W., L.S. William, G. Corinna, H. Diane, 2006. Land use and land cover mapping from diverse data sources for an arid urban environments. Computers, Environment and Urban Systems, 30; 320– 346. Foody, G.M. 2004. Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogrammetric Engineering and Remote Sensing, 70; 627–633. Foody, G.M., A. Mathur, 2004. A relative evaluation of multiclass image classification by support vector machines. IEEE Trans Geosciences Remote Sensing, 42; 1335–1343 Friedman, M., A. Kandel, 1999. Introduction to Pattern Recognition: Statistical, Structural, Neural, and Fuzzy Logic Approaches. World Scientific Pub Co Inc, 1999. Ghimire, S., H. Wang, 2012. Classification of image pixels based on minimum distance and hypothesis testing. Computational Statistics and Data Analysis, 56; 2273– 2287. Gualtieri, J.A., R.F. Cromp, 1998. Support vector machines for hyperspectral remote sensing classification. In: Proceedings of the 27th AIPR Workshop: Advances in Computer Assisted Recognition, Washington, DC, 27 October. SPIE, Washington, DC, pp. 221–232. Halder, A., A. Ghosh, S. Ghosh, 2011. Supervised and unsupervised landuse map generation from remotely sensed images using ant based systems. Applied Soft Computing. In press. Han, D., L. Chan, N. Zhu, 2007. Flood forecasting using support vector machines. Journal of Hydroinformatics, 9; 267–276. Hannv, Z., J. Qigang, X. Jiang, 2013. Coastline Extraction Using Support Vector Machine from Remote Sensing Image. Journal of Multimedia, 8; 175-182. Hopkins, P.F., A.L. Maclean, T.M. Lillesand, 1988. Assessment of thematic mapper imagery for forestry application under lake states conditions, Photogrameteric Engineering and Remote Sensing, 54; 61-68. Huang, C., L.S. Davis, J.R.G. Townshend, 2002. An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23; 725–749. Jacqueminet, C., S. Kermadi, K. Michel, D. Béal, M. Gagnage, F. Branger, S. Jankowfsky, I. Braud, 2013. Land cover mapping using aerial and VHR satellite images for distributed hydrological modelling of periurban catchments: Application to the Yzeron catchment (Lyon, France). Journal of Hydrology, 485; 68–83. Jensen, J. 2005. Introductory digital image processing: A remote sensing perspective (3rd ed.). Upper Saddle River, NJ: Prentice Hall. Kavzoglu, T., P.M. Mather, 2003. The use of backpropagating artificial neural networks in land covers classification. International Journal of Remote Sensing, 24; 4907-4938. Kruse, F.A., A.B. Lefkoff, J.B. Boardman, K.B. Heidebrecht, A.T. Shapiro, P.J. Barloon, A.F.H. Goetz, 1993. The spectral image processing system (SIPS) - interactive visualization and analysis of imaging spectrometer data. Remote Sensing of the Environment, 44; 145 - 163. Liu, Z.K., J.Y. Xiao, 1991. Classification of remotelysensed image data using artificial neural networks, International Journal of Remote Sensing, 12; 2433– 2438. Lu, D., Q. Weng, 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28; 823-870. Luc, B., B. Deronde, P. Kempeneers, W. Debruyn, S. Provoost, 2005. Optimized Spectral Angle Mapper classification of spatially heterogeneous dynamic dune vegetation, a case study along the Belgian coastline. The 9th International Symposium on Physical Measurements and Signatures in Remote Sensing (ISPMSRS). Beijing, pp 17-19. Mantero, P., G. Moser, S.B. Serpico, 2005. Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Transactions on Geoscience and Remote Sensing, 43; 559–570. Mazer, A.S., M. Martin, M. Lee, J.E. Solomon, 1988. Image processing software for imaging spectrometry analysis, Remote Sensing of the Environment, 24; 201- 210. Melgani, F., L. Bruzzone, 2004. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions Geosciences Remote Sensing, 7; 1778–1790. Mountrakis, G., J. Im, C. Ogole, 2011. Support vector machines in remote sensing: Areview. ISPRS Journal of Photogrammetry and Remote Sensing, 66; 247–259. Mulder, N., L. Spreeuwers, 1991. Neural networks applied to the classification of remotely sensed data, in: Proceedings of IGARSS, Espoo, Finland. Munoz-Marf, J., L. Bruzzone, G. Camps-Vails, 2007. A support vector domain description approach to supervised classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 45; 2683-2692. Oommen, T., D. Misra, N.K.C. Twarakavi, A. Prakash, B. Sahoo, S. Bandopadhyay, 2008. An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing. Mathematical Geosciences, 40; 409-424. Otukei, J.R., T. Blaschke, 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; S27–S31. Pal, M., P.M. Mather, 2005. Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26(5); 1007–1011. Perumal, K., R. Bhaskaran, 2010. Supervised classification performance of multispectral images. Journal of Computing, 2(2); 124-129. Petropoulos, G., W. Knorr, M. Scholze, L. Boschetti, G. Karantounias, 2010. Combining ASTER multispectral imagery analysis and support vector machines for rapid and cost-effective post-fire assessment: a case study from the Greek wildland fires of 2007. Natural Hazards Earth System Sciences, 10; 305–317. Petropoulos, G.P., C. Kontoes, I. Keramitsoglou, 2011. Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using Support VectorMachines, 13; 70–80. Qiu, F., J.R. Jensen, 2004. Opening the black box of neural networks for remote sensing image classification. International Journal of Remote Sensing, 25; 1749-1768. Rajesh, B.T., M. Yuji, 2009. Urban mapping, accuracy, & image classification: A comparison of multiple approaches in Tsukuba City, Japan. Applied Geography, 29; 135–144. Remesan, R., M. Bray, M.A. Shamim, H. DaWei, I. Cluckie, Y. Chen, V. Babovic, L. Konikow, A. Mynett, S. Demuth, 2009. Rainfall– runoff modeling using a wavelet-based hybrid SVM scheme. IAHS Press. Richards, J.A. 1999. Remote Sensing Digital Image Analysis, Springer-Verlag, Berlin, p.240. Richards, J.A., X. Jia, 2006. Remote Sensing Digital Image Analysis: An Introduction. Springer Verlag. Salberg, B., R. Jenssen, 2012. Land-cover classification of partly missing data using support vector machines. International Journal of Remote Sensing, 33; 4471- 4481. Sanchez-Hernandez, C., D.S. Boyd, G.M. Foody, 2007. Mapping specific habitats from remotely sensed imagery: Support vector machine and support vector data description based classification of coastal saltmarsh habitats. Ecological Informatics, 2; 83–88. Schalkoff, R.J. 1997. Artificial Neural Networks. McGraw- Hill Companies. Schneider, A, 2012. Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sensing of Environment, 124; 689-704. Shim, D, 2014. Remote sensing place: Satellite images as visual spatial imaginaries. Geoforum, 51; 152–160. Sohn, Y., N.S. Rebello, 2002. Supervised and Unsupervised Spectral Angle Classifiers. Photogrammetric Engineering & Remote Sensing, 68; 1271-1280. Srivastava, P., D. Han, M.A. Rico-Ramirez, M. Bray, T. Islam, 2012. Selection of classification techniques for land use/land covers change investigation. Advances in Space Research, 50; 1250–1265b. Srivastava, P., G. Kiran, M. Gupta, N. Sharma, K. Prasad, 2012. A study on distribution of heavy metal contamination in the vegetables using GIS and analytical technique. International Journal of Ecology and Development, 21; 89–99a. Szuster, B.W., Q. Chen, M. Borger, 2011. A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Applied Geography, 31; 525–532. Tigges, J., T. Lakes, P. Hostert, 2013. Urban vegetation classification: Benefits of multitemporal RapidEye satellite data. Remote Sensing of Environment, 136; 66- 75. Vapnik, V.N. 2000. The Nature of Statistical Learning Theory. Journal of Contaminant Hydrology, 120; 129- 140. Vapnik, V.N., A.Y. Chervonenkis, 1971. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, 16; 264p. Volpi, M., D. Tuia, F. Bovolo, M. Kanevski, L. Bruzzone, 2013. Supervised change detection in VHR images using contextual information and support vector machines. International Journal of Applied Earth Observation and Geoinformation, 27; 77-85. Xing, E.P., A.Y. Ng, M.I. Jordan, S. Russell, 2003. Distance metric learning, with application to clustering with side-information. In Advances in NIPS. Cambridge, MA, USA: MIT Press. Zare Abyaneh, H., A. Moghaddamnia, M. Bayat Varkeshi, S. Marofi, O. Kisi, 2011. Performance Evaluation of ANN and ANFIS Models for Estimating Garlic Crop Evapotranspiration. Journal of Irrigation and Drainage Engineering, 137 (5); 280–286. Zhang, Y., D. Huang, M. Ji, F. Xie, 2011. Image segmentation using PSO and PCM with Mahalanobis distance. Expert Systems with Applications, 38; 9036– 9040. Zhou, F., A. Zhang, L. Townley-Smith, 2013. A data mining approach for evaluation of optimal time-series of MODIS data for land cover mapping at a regional level. ISPRS Journal of Photogrammetry and Remote Sensing, 84;114–129. Zhou, Z., S. Wei, X. Zhang, X. Zhao, 2007. Remote sensing image segmentation based on self organizing map at multiple scales, in: Proceedings of SPIE Geoinformatics: Remotely Sensed Data and Information, USA, pp; 122–126. | ||
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