Multispectral satellite images are more efficient and a suitable method of obtaining information about land, because it can captures an image at specific frequency across the spectrum. This spectral image can allow extraction of further information about ground survey than the other traditional image. Classification of multispectral image consists of image processing and classification method. Here, an efficient technique is proposed for classifying the multispectral images using fuzzy incorporated hierarchical clustering with RVM classifier. In the proposed technique, first the multispectral satellite image is subjected to set of pre-processing steps, which are used to transform an image into suitable form that is easier for segmentation and classification. Subsequently, the pre-processed image is segmented using fuzzy incorporated hierarchical clustering. Then, the proper kernel function is selected for RVM clustered output. Finally the multispectral image is classified into multiple sectors based on the training data. The classification is used in the application of land degradation studies, environmental damage, resource management and other environmental application.

1. 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.
2. M. Fernandez-Delgado, P. Carrion, E. Cernadas, J. F. Galvez, Pilar Sa-Otero (2004)-"Improved Classification of Pollen texture Images using SVM and MLP"
3. Shaomei Yang and Qian Zhu (2008)-"Research on Comparison and Application of SVM and FNN Algorithm".
4. Dong-Hyuk Shin, Rae-Hong Park, Senior Member, IEEE, Seungjoon Yang, Member, IEEE, and Jae-Han Jung, Member, IEEE (2005)-"Block-Based Noise Estimation Using Adaptive Gaussian".
5. Chen, D. Chen, and D Blostein (2007)-"Wavelet-Based Classification of Remotely Sensed Images".
6. Wei Yu, Jason Fritts and Fangting Sun (2000),"A Hierarchical Image Segmentation Algorithm".
7. Geva,A. B(1999)-"Hierarchical unsupervised fuzzy clustering ".
8. 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.
9. Zhihua Zhang a, _, GangWangb, Dit-YanYeung b, GuangDai b, FrederickLochovsky, " A regularization framework for multiclass classification: A deterministic annealing approach ", Elsevire,pattern Recognization(2010).
10. B. Sowmya and B Sheelarani , "Land cover classification using reformed fuzzy C-means", Sadhana, Vol. 36, No. 2, pp. 153–165, 2011.
11. B. Yogameena, S. Veera Lakshmi, M. Archana and S. Raju Abhaikumar, "Human Behavior classification Using Multi-Class Relevance Vector Machine", Journal of Computer Science 6 (9): 1021-1026, 2010,ISSN 1549-3636© 2010 Science Publications.
12. 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.
13. Mathieu Fauvel ¤¦, Jocelyn Chanussot ¤ and Jon Atli Benediktsson "evaluation of kernels for multiclass classification of hyperspectral remote sensing data, ¤Laboratoire des Images et des Signaux.
14. S. Chen, S. R. Gunn, and C. J. Harris,' The Relevance Vector Machine Technique for Channel Equalization Application', IEEE transactions on neural networks, vol. 12, no. 6, november 2001.
15. Fereidoun A. Mianji, "Robust Hyperspectral Classification Using Relevance Vector Machine", IEEE transactions on geoscience and remote sensing, vol. 49, no. 6, june 2011.
16. Begüm Demir,," Hyperspectral Image Classification Using Relevance Vector Machines", IEEE geoscience and remote sensing letters, vol. 4, no. 4, october 2007.
17. S. Chen, S. R. Gunn, and C. J. Harris,' The Relevance Vector Machine Technique for Channel Equalization Application', IEEE transactions on neural networks, vol. 12, no. 6, november 2001.
18. Pak-Kin Wong, Qingsong Xu,' Rate-Dependent Hysteresis Modeling and Control of a Piezostage Using Online Support Vector Machine and Relevance Vector Machine', IEEE transactions on industrial electronics, vol. 59, no. 4, april 2012.
19. Behnood Gholami,' Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging, IEEE transactions on biomedical engineering, vol. 57, no. 6, june 2010.
20. Mahesh Pal and Giles M. Foody,' Evaluation of SVM, RVM and SMLR for Accurate Image Classification With Limited Ground Data', IEEE journal of selected topics in applied earth observations and remote sensing, vol. 5, no. 5, october 2012.
21. Andreas Ch. Braun, Uwe Weidner, and Stefan Hinz,' Classification in High-Dimensional Feature Spaces—Assessment Using SVM, IVM and RVM With Focus on Simulated EnMAP Data', IEEE journal of selected topics in applied earth observations and remote sensing, vol. 5, no. 2, april 2012.
22. Mianji, F. A. ; Ye Zhang; Babakhani, A. ,' Nonlinear discriminant analysis and RVM for efficient classification of small land-cover patches ',IEEE Conference Publications Communications and Signal Processing (ICCSP), 2011 .
23. Uehara, Hisashi; Watanabe, Hideyuki; Katagiri, Shigeru; Ohsaki, Miho,' Comparison between Minimum Classification Error training and Relevance Vector Machine ',IEEE Conference Publications TENCON 2012 - 2012.
24. Babaeean, A. ; Tashk, A. B. ; Bandarabadi, M. ; Rastegar, S. ,' Target Tracking Using Wavelet Features and RVM Classifier', IEEE Conference Publications Natural Computation, 2008. ICNC '08.
25. G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens. , vol. 43, no. 6, pp. 1352–1362, Jun. 2005.
26. M. E. Tipping, "The relevance vector machine," in Advances in Neural Information ProcessingSystems , vol. 12, S. A. Solla, T. K. Leen, and K. -R. Müller, Eds. Cambridge, MA: MIT Press, 2000.

Classification Rvm Multispectral Satellite Image clustering.