Full Text

Abstract

A Satellite image classification is a significant method used in remote sensing for the automated analysis and pattern recognition of satellite data, which facilitate the automated understanding of a large amount of information. These days, there exist many types of classification algorithms, such as parallelepiped and minimum distance classifiers, but it is still essential to get better their performance in terms of correctness rate. On the additional hand, in excess of the last few decades, cellular automata have been used in remote sensing to implement procedure related to simulation. While there is little preceding research of cellular automata related to satellite image classification, they offer much reward that can improve the results of classical categorization algorithms. This document discuss the expansion of a new organization algorithm based on cellular automata which not only improve the classification accuracy rate in dependency images by using related techniques but also offers a hierarchical classification of pixels divided into levels of association degree to each class and includes a spatial edge discovery method of classes in the satellite image.

References

1. Joseph, George. Fundamentals of remote sensing. Universities Press, 2005.

2. Jensen, John R. Remote sensing of the environment: An earth resource perspective 2/e. Pearson Education India, 2009.

3. Rees, William Gareth. Physical principles of remote sensing. Cambridge University Press, 2013.

4. Akgün, Aykut, A. HüsnüEronat, and Necdet Turk. \"Comparing Different Satellite Image Classification Methods: An Application InAyvalýk District, Western Turkey.\" XXth International Congress for Photogrammetry and Remote Sensing, Istanbul, Turkey. 2004.

5. Hwanjo Yu, Jiong Yang, Jiawei Han "Classifying Large Data Sets Using SVMs with Hierarchical Clusters", SIGKDD ’03. August 24-27. 2003.

6. Jiawei Han, Micheline Kamber, "Data Mining Concepts and Techniques" Second Edition, Morgan Kaufmann Publishers,2006, Elsevier Inc.

7. Sang Rok Lee, "A Coarse-to-Fine Approach for Remote-Sensing Image Registration Based on a Local Method", International Journal on Smart Sensing And Intelligent Systems, vol. 3, no. 4, December 2010.

8. Lin, Keng-Pei, and Ming-Syan Chen. \"On the design and analysis of the privacy-preserving SVM classifier.\" Knowledge and Data Engineering, IEEE Transactions on 23.11 (2011): 1704-1717.

9. Wu, Mu-Lin, et al. \"Blended tools for remote sensing education.\" Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International. IEEE, 2007.

10. Vinit Kumar et. al , "Land Features Extraction from Image by the Probability Concept of Honey Bees: A Remote Sensing Perspective", IJCAA, Volume 3, Issue, June 2013.

11. De Casro, N., Fundamentals of natural computing: basic concepts, algorithms,

12. Lila Kari, GrzegorzRozenberg, \" The Many Facets of Natural Computing,\" Communication of the ACM,vol. 51, no.10,October2008.

13. Harish Kundra et.al, "Hybrid Algorithm of CS and ACO for Image Classification of Natural Terrain Features", International Journal of Advances in Computer Science and Communication Engineering (IJACSCE)Vol I Issue I (November 2013).

14. Akansha, "The International Archives of the Photo grammetry, Remote Sensing and Spatial Information Sciences, Vol. 38, Part II Commission VI, WG VI/4

15. Lavika et.al, "Dynamic Model of Blended Biogeography Based Optimization for Land Cover Feature Extraction", ACM, 2012.

16. Arpita Sharma et. Al , "Cuckoo Search Based Decision Fusion Techniques for Natural Terrain Understanding", ACM, 2014.

Keywords

Image categorization, Pattern identification, Distant (Remote) sensing.