Call for Paper - July 2022 Edition
IJCA solicits original research papers for the July 2022 Edition. Last date of manuscript submission is June 20, 2022. Read More

Classification of Multispectral Satellite Images using Clustering With SVM Classifier

Print
PDF
International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 35 - Number 5
Year of Publication: 2011
Authors:
S. V. S. Prasad
Dr. T. Satya Savitri
Dr. I. V. Murali Krishna
10.5120/4399-6107

S V S Prasad, Dr. Satya T Savitri and Dr. Murali I V Krishna. Article: Classification of Multispectral Satellite Images Using Clustering With SVM Classifier. International Journal of Computer Applications 35(5):32-44, December 2011. Full text available. BibTeX

@article{key:article,
	author = {S. V. S. Prasad and Dr. T. Satya Savitri and Dr. I. V. Murali Krishna},
	title = {Article: Classification of Multispectral Satellite Images Using Clustering With SVM Classifier},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {35},
	number = {5},
	pages = {32-44},
	month = {December},
	note = {Full text available}
}

Abstract

Multi-spectral satellite imagery is an economical, precise and appropriate method of obtaining information on land use and land cover since they provide data at regular intervals and is economical when compared to the other traditional methods of ground survey and aerial photography. Classification of multispectral remotely sensed data is investigated with a special focus on uncertainty analysis in the produced land-cover maps. Here, we have proposed an efficient technique for classifying the multispectral satellite images using SVM into land cover and land use sectors. In the proposed classification technique initially pre-processing is done where the input image is subjected to a set of pre-processing steps which includes Gaussian filtering and RGB to Labcolorspace image conversion. Subsequently, segmentation using fuzzy incorporated hierarchical clustering technique is carried out. Then training of the SVM is carried out in the training data selection procedure and finally the classification step, where the cluster centroids are subjected to the trained SVM to obtain the land use and land cover sectors. The experimentation is carried out using the multi-spectral satellite images and the analysis ensures that the performance of the proposed technique is improved compared with traditional clustering algorithm.

References

  • K Perumal and R Bhaskaran , “SVM-Based Effective Land Use Classification System For Multispectral Remote Sensing Images”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 6, No. 2, pp.95-107, 2009.
  • Jan Knorn, Andreas Rabe, Volker C. Radeloff, Tobias Kuemmerle, Jacek Kozak, Patrick Hostert, "Land cover mapping of large areas using chain classification of neighboring Landsat satellite images", Remote Sensing of Environment, Vol. 118, pages 957-964 , 2009.
  • Xiaochen Zou, Daoliang Li, "Application of Image Texture Analysis to Improve Land Cover Classification", WSEAS Transactions on Computers, Vol. 8, No. 3, pp. 449-458, March 2009.
  • Reda A. El-Khoribi, "Support Vector Machine Training of HMT Models for Multispectral Image Classification", IJCSNS International Journal of Computer Science and Network Security, Vol.8, No.9, pp.224-228, September 2008.
  • B Sowmya and B Sheelarani , “Land cover classification using reformed fuzzy C-means”, Sadhana, Vol. 36, No. 2, pp. 153–165, 2011.
  • V.K.Panchal, Parminder Singh, Navdeep Kaur and Harish Kundra, “Biogeography based Satellite Image Classification”, International Journal of Computer Science and Information Security IJCSIS, Vol. 6, No. 2, pp. 269-274, November 2009.
  • Huang B, Xie C, Tay R, Wu B, 2009, "Land-use-change modeling using unbalanced support-vector machines" , Environment and Planning B: Planning and Design , Vol.36, No.3, pp.398-416,2009.
  • James A. Shine and Daniel B. Carr, "A Comparison of Classification Methods for Large Imagery Data Sets", JSM 2002 Statistics in an ERA of Technological Change-Statistical computing section, New York City, pp.3205-3207, 11-15 August 2002.
  • D. Lu, Q. Weng, "A survey of image classification methods and techniques for improving classification performance", International Journal of Remote Sensing, Vol. 28, No. 5, pp. 823-870, January 2007.
  • M. Govender, K. Chetty, V. Naiken and H. Bulcock, "A comparison of satellite hyperspectral and multispectral remote sensing imagery for improved classification and mapping of vegetation", Water SA, Vol. 34, No. 2, April 2008.
  • Jasinski, M. F., "Estimation of subpixel vegetation density of natural regions using satellite multispectral imagery", IEEE Transactions on Geoscience and Remote Sensing, Vol. 34, pp. 804–813, 1996.
  • C. Palaniswami, A. K. Upadhyay and H. P. Maheswarappa, "Spectral mixture analysis for subpixel classification of coconut", Current Science, Vol. 91, No. 12, pp. 1706 -1711, 25 December 2006.
  • Ming-Hseng Tseng, Sheng-Jhe Chen, Gwo- Haur Hwang, Ming-Yu Shen, "A genetic algorithm rule-based approach for land-cover classification", Journal of Photogrammetry and Remote Sensing ,Vol.63, No.2, (3), pp. 202-212, 2008.
  • Pall Oskar Gislason, Jon Atli Benediktsson, Johannes R. Sveinsson, "Random Forests for land cover classification", Pattern Recognition Letters,Vol.27, No.4, (3), pp. 294-300, 2006.
  • Hua-Mei Chen, Varshney, P.K. and Arora, M.K, “Performance of mutual information similarity measure for registration of multitemporal remote sensing images “, IEEE Transactions on Geoscience and Remote Sensing, Vol.41 No.11, pp. 2445 – 2454, 2003.
  • Cristianini, Nello and Shawe-Taylor, John, “An Introduction to Support Vector Machines and other kernel based learning methods", Cambridge University Press, Cambridge, 2000.
  • Li Zhuo, Jing Zheng, Fang Wang, Xia Li, Bin Ai, Junping Qian, "A Genetic Algorithm Based Wrapper Feature Selection Method For Classification Of Hyperspectral Images Using Support Vector Machine", The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Vol. XXXVII, No. B7, pp.397-402, 2008.
  • S. C. Johnson, "Hierarchical Clustering Schemes", Psychometrika, Vol.2, pp.241-254, 1967.
  • J. C. Dunn (1973): "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters", Journal of Cybernetics, Vol. 3, pp.32-57, 1973.
  • R.A. Haddad and A.N. Akansu, "A Class of Fast Gaussian Binomial Filters for Speech and Image Processing," IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 39, pp 723-727, March 1991.
  • Hunter and Richard Sewall ,"Accuracy, Precision, and Stability of New Photo-electric Color-Difference Meter", Proceedings of the Thirty-Third Annual Meeting of the Optical Society of America, Vol. 38(12), 1948.