Satellite Image Classification Based on Fuzzy with Cellular Automata

International Journal of Electronics and Communication Engineering

© 2015 by SSRG - IJECE Journal

Volume 2 Issue 3

Year of Publication : 2015

Authors : Harikrishnan.R and S.Poongodi

10.14445/23488549/IJECE-V2I3P126

How to Cite?

Harikrishnan.R and S.Poongodi, "Satellite Image Classification Based on Fuzzy with Cellular Automata," SSRG International Journal of Electronics and Communication Engineering, vol. 2,  no. 3, pp. 34-38, 2015. Crossref, https://doi.org/10.14445/23488549/IJECE-V2I3P126

Abstract:

Satellite image classification is a significant technique used in remote sensing for the computerized study and pattern recognition of satellite information, which make possible the routine explanation of a huge quantity of information. Nowadays cellular automata are implemented for simulation of satellite images and also cellular automata relates to categorization in satellite image is used simultaneously. Based on information of stored image value to the cell and dimension of neighbourhood cells. Inoder fine tune classification rate of cellular automata algorithm fuzzy rules with cellular automata are used . In this paper cellular with fuzzy rules have been implemented for classifying the satellite image and quality of classified image is analyzed.

Keywords:

Cellular automata , membership function

References:

[1] Moisés Espínola, José A. Piedra-Fernández, Rosa Ayala, Luis Iribarne, and James Z. Wang, “Contextual and Hierarchical Classification of Satellite Images Based on Cellular Automata”, IEEE Trans on geosciences and remote sensing,journals of information sciences 2014.
[2] F. Agüera, M. A. Aguilar, and F. J. Aguilar, “Detecting greenhouse changes from Quick Bird imagery on the Mediterranean coast,” Int. Journal of Remote Sens., vol. 27, no. 21, pp. 4751–4767, Nov. 2006.
[3] Leguizamón,“Modelling land features dynamics by using cellular automata techniques,” in Proc. ISPR Tech. Commission 7Mid-Term Symp.“From pixels to Processes”, 2006, pp. 497–501.
[4] B. Mojaradi, C. Lucas, and M. Varshosaz,“Using learning cellular automata for post classification satellite imagery,” Int. Arch. Photogramm.Remote Sens. Spatial Inf. Sci., vol. 35, no. 4, pp. 991–995, 2004.
[5] J. A. Piedra-Fernández, M. Cantón-Garbín, and J. Z. Wang, “Feature selection in AVHRR ocean satellite images by means of filter methods,”IEEE Trans. Geosci. Remote Sens., vol. 48, no. 12, pp. 4193–4203,Dec. 2010.
[6] Bo Li, Graeme G. Wilkinson & Souheil Khaddaj “Cell-based Model For GIS Generalization” School of Computing & Information Systems Kingston University Penrhyn Road, Kingston upon Thames Surrey, KT1 2EE UK..
[7] Deepak Ranjan Nayak,Sumit Kumar Sahu ,Jahangir Mohammed “ A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model”, International Journal of Computer Applications (0975 – 8887) Volume 84 – No 10, December 2013.
[8] Sreela  Chandran,  S  Natheldha    Mary     Navina”Cellular Automata Based Denoising and Fuzzy Logic Based Edge Detection”, International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 2, Issue 2, February 2013.
[9] Moises Espınola, Rosa Ayala, Saturnino Leguizamon, Luis Iribarne, and Massimo MenentiO,” Cellular Automata Applied in Remote Sensingto Implement Contextual Pseudo fuzzyClassification”, journal of  Cellular Automata Applied in Remote Sensing,pp 312-321.
[10] Saturnino Leguizamón  , Moisés Espínola,      RosaAyala, Luis Iribarne   and   Massimo    Menenti” Characterization of Texture in Images by Using a Cellular Automata Approach” Applied Computing Group, University of Almería, Spain,pp 522-533.