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Reseach Article

Classification of a Satellite Rural Image based on Fractal Dimension using Box Counting Method

by DR.Ambika, AG Ananth
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 16 - Number 5
Year of Publication: 2011
Authors: DR.Ambika, AG Ananth
10.5120/2041-2640

DR.Ambika, AG Ananth . Classification of a Satellite Rural Image based on Fractal Dimension using Box Counting Method. International Journal of Computer Applications. 16, 5 ( February 2011), 45-48. DOI=10.5120/2041-2640

@article{ 10.5120/2041-2640,
author = { DR.Ambika, AG Ananth },
title = { Classification of a Satellite Rural Image based on Fractal Dimension using Box Counting Method },
journal = { International Journal of Computer Applications },
issue_date = { February 2011 },
volume = { 16 },
number = { 5 },
month = { February },
year = { 2011 },
issn = { 0975-8887 },
pages = { 45-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume16/number5/2041-2640/ },
doi = { 10.5120/2041-2640 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:04:08.039442+05:30
%A DR.Ambika
%A AG Ananth
%T Classification of a Satellite Rural Image based on Fractal Dimension using Box Counting Method
%J International Journal of Computer Applications
%@ 0975-8887
%V 16
%N 5
%P 45-48
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fractal geometry has sparked considerable interest for analyzing remote sensing imageries. Fractal models have been used in several image processing and pattern recognition applications such as texture analysis and classification. Applications of fractal geometry in remote sensing rely heavily on estimation of the fractal dimension (D). When land areas are clustered into groups of similar land cover, one of the most important things is to extract the key features of a given image. The present paper uses box counting method for determining the fractal dimension D for the classification of remote sensing imageries and identifying the various features present in the imageries. The features present in a typical remotely sensed Rural image such as Water bodies, Dry land, Vegetation, Forest, Rocky regions and Housing etc have been studied using the fractal dimensions. The studies show that the box counting method provide a very clear distinction between the fractal dimensions determined for the different features present in the satellite Rural imageries.

References
  1. ZHU Ji, LIN Ziyu, WANG Angsheng, CUI Peng, 2006. An approach to extracting fractal in remote sensing image. WUJNS, Vol.11, No.3,606-610
  2. Mandelbrot, B.B., 1977, Fractals: Form, Chance and Dimension. San Francisco, CA: W.H. Freeman and Company.
  3. W. SUN, G. XU, P. GONG and S. LIANG, 2006. Fractal Analysis of Remotely Sensed Images: A review of methods and applications, Review Article, International Journal of Remote Sensing, Vol. 27, No. 22, 4963-4990
  4. F. Berizzi, P. Gamba, A. Garzelli, F. Dell’ Acqua 2002. Fractal Behavior of Sea SAR ERS1 Images, IEEE transactions.
  5. Qulin TAN, Yun SHAO, Xiangtao FAN.,2002. A Novel Edge Detection Algorithm for Remote Sensing Images based on the self-similarity of Fractal Character, IEEE transactions.
  6. Mandelbrot B. B., 1983. The Fractal Geometry of Nature. New York: Freeman, 20-40.
  7. Ye, J., Chen, B.Z., 2001. The application of the fractal theory in the city research. Urban Planning Forum 4, 38-42.
  8. Jiang, S.G., 2004. Studies on fractal urban form using GIS and remote sensing images of Beijing: theory, method and practice. Paper for the Bachelor’s degree of Peking University.
  9. Peng, R.D., Xie, H.P., Ju, Y., 2004. Computation method of Fractal dimension for 2-D digital image. Journal of China University of Mining & Technology 33: 19-24.
  10. Chen, Y., Chen, L., 1998. The fractal geometry. Beijing: Earthquake Publishing.
Index Terms

Computer Science
Information Sciences

Keywords

Fractal Dimension Remote Sensing Box