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Comparison of ANN and SVM methods in extraction Land Use/ Land Cover maps from Landsat 8 satellite image (Case Study: Sufi Chay Basin)

Document Type : Research Paper

Authors

1 Group Geomorphology Tabriz University

2 Department of Remote Sensing and GIS university

3 Remote sensing and GIS

4 Remote sensing and GIS Azarbaijan Regional Water Company

Abstract

Land use and land cover maps are necessary for planning and natural resources management. In the way, remote sensing data have special place because of providing update data, repetitive covers and low cost images. Therefore Optimum Land Image/ Thermal Infrared Sensor were used to map land-use and land-cover in 1 and 2 level. Because of, this images are new thus radiometric correct was used ERDAS software model maker. Also Normalize Difference Vegetation Index (NDVI), Bare Soil Index (BI) and Principal Component Analyze (PCA) were used as inputs to improve classification accuracy. On the other hand kernels functional and polynomial ranks of Support Vector Machine method evaluated in side others bands and the best result of SVM method compared with Artificial Neural Network (ANN). The results indicated that SVM method has accuracy: 92% with Kappa Coefficient: 0.91 and ANN method has accuracy: 89% with kappa coefficient: 0.87 also SVM method has a good performance in the regions that, classes show similar spectral behavior.

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References
منابع
ـ علوی­پناه، کاظم (1389)، «کاربرد سنجش از دور در علوم زمین»، انتشارات دانشگاه تهران، چاپ سوم.
ـ فاطمی، ب.؛ رضایی، ی. (1391) ، «مبانی سنجش از دور»، انتشارات آزاده، چاپ سوم .
- Atkinson P.M., Tatnall, A.R.L., (1997), “Ne2ural Networks in Remote Sensing”, Int. J. Remote Sensing, Vol. 18, No. 4, pp. 699-709.
- Brian, W., Qi Chen, Z., Borge, M., (2011), “A comparison of classification techniques to support land cover and land use analysis in tropical coastal zone”, Applied Geography 31, 525-53.
- Chavez, p., (1996), “Image-based atmospheric corrections-Revisited and improved”, Photogramm, Eng. Remote Sensing, Vol. 62, pp. 1025–1036, Sept.
- Colby, J.D., (1991), “Topographic Normalization in Rugged Terrain”, Photogrammetric Engineering & Remote Sensing 57 (5), 531-537.
- Congalton, R.G., Green, K. (1999), “Assessing the accuracy of remotely sensed data: principles and practices”, Boca Raton: Lewis Publications.
- Fody, M.G., (1996), “Relating the Land-Cover Composition of Mixed Pixels to Artificial Neural Network Classification Output”, Photogrammetric Engineering & Remote Sensing, Vol. 62, No. 5, pp. 491-499.
- Foody, M.G., and Mathur, A. (2004)a. “A Relative Evaluation of Multiclass Image Classification by Support Vector Machines”. IEEE Transactions on Geoscience and Remote Sensing, 42, 1335-1343.
- Gandini, M.L., & and Usunoff, E.J., (2004), “SCS curve number estimation using remote sensing NDVI in a GIS environmental”, Journal of Environmental Hydrology, (12), 168-179.
- 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.
- Nitze, A., Schulthess, B., Asche, H., (2012), “Comparison of Machine Learning  Algorithms Random Forest, Artificial Neural Network and Sopport Vector Machine to Maximum Likelihood for Suporvised Crop Type Classification”, Proceedings of the 4th Geobia , May 7-9, Rio de Janeiro-Brazil. P. 035.
- Noori, R., Abdoli, M.A., Ameri A., and Jalili-Ghazizade, M., (2008), “Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: A case study of Mashhad”, Environmental Progress and Sustainable Energy, 28 (2), 249-258.
- Rao, S., Sharma,A,.(2013). Cost Parameter Analysis and Comparison of Linear Kernel and Hellinger Kernel Mapping of SVM on Image Retrieval and Effects of Addition of Positive Images, International Journal of Computer Applications (0975-8887) Volume 73– No.2.
- Roy, P.S., Sharma, K.P., JAIN, A., (1996), “Stratification of density in dry deciduous forest using satellite remote sensing digital data-An approach based on spectral indices”, J. Biosci., Vol. 21, pp 723-734. © Printed in India.
- Srivastava, P.K., Han. D., Rico-Ramirez, M.A., Bray, M., Islam, T. (2012), “Selection of classification techniques for land use/land cover change investigation”, Advances in Space Research (50) 1250-1265.
- Shalkoff, R.J. (1997), “Artificial Neural Networks”, McGraw-Hill Companies Pub.
- Mantero P., Moser, G., Serpico, S.B., (2005), “Partially supervised classification of remote sensing images through SVM-based probability density estimation”, IEEE Trans. on Geoscience and Remote Sensing, Vol. 43, No. 3 , 559-570.
- Srivastava, D.K., Bhambhu, L., (2009), “Data classification using support vector machine”, Journal of Theoretical and Applied Information Technology, 1-7.
- Vapnik, V.N. (1999), “The Nature of Statistical Learning Theory”, 1-339, Second Edition. (New York: Springer-Verlag).
- Warner, T., (2005), “Hyperspherical Direction Cosine Change Vector Analysis”, International Journal of Remote Sensing, Vol, 26, pp.1201-1215.
- Yan, Y., (2003), “Object-based Classification of Remote Sensing Data for change detection”, www.elsevier.com.
- Zhu, G., Blumberg, D.G., (2002), “Classification using ASTER data and SVM algorithms: the case study of Beer Sheva”, Remote Sensing of Environment, 80(2), 233-240.
- http://landsat.usgs.gov/band_designations_landsat_satellites.php.
- landsat7.usgs.gov/Landsat8_Using_Product.php.
- http://www.gisagmaps.com/landsat-8-atco-guide.
Journal of Geography and Planning
Volume 19, Issue 52 - Serial Number 52
June 2015
Pages 163-183
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