CFP last date
20 June 2024
Call for Paper
July Edition
IJCA solicits high quality original research papers for the upcoming July edition of the journal. The last date of research paper submission is 20 June 2024

Submit your paper
Know more
Reseach Article

Classification of Satellite Images through Gabor Filter using SVM

by Manali Jain, Amit Sinha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 7
Year of Publication: 2015
Authors: Manali Jain, Amit Sinha

Manali Jain, Amit Sinha . Classification of Satellite Images through Gabor Filter using SVM. International Journal of Computer Applications. 116, 7 ( April 2015), 18-21. DOI=10.5120/20348-2534

@article{ 10.5120/20348-2534,
author = { Manali Jain, Amit Sinha },
title = { Classification of Satellite Images through Gabor Filter using SVM },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 7 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 18-21 },
numpages = {9},
url = { },
doi = { 10.5120/20348-2534 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:56:27.261283+05:30
%A Manali Jain
%A Amit Sinha
%T Classification of Satellite Images through Gabor Filter using SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 7
%P 18-21
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

The classification of satellite image has various issues including image quality. The aim of classifying satellite image is to provide better understanding of image . The classification of image depends on various features. the present paper focuses on five categories of satellite image such as residential area, agriculture, desert, mountain and forest. The objective of this paper is to study the use of texture, color, shape as an image feature for pattern retrieval. We propose the novel approach to extract features of image through Gabor filter feature vector i. e color, texture, shape and classification of image through support vector machine (SVM) with the use of Gaussian radial basis kernel function. The proposed methodology gives the better accuracy up to 98. 5% over DCT Gabor based classification. The work is useful and may be embedded with different applications like residential purposes and government planning commission and national environment mission etc.

  1. J. Daugman, "High confidence visual recognition of persons by a test of statistical independence," IEEE Trans. on PAMI, vol. 25, no. 9, 1993.
  2. K . Messer and et al . , " Face authentication test on the BANCA database," in Int Conf on Pattern Recognition (ICPR), 2004.
  3. Jain, Y. Chen, and M. Demirkus, "Pores and ridges: Fingerprint matching using level 3 features," IEEE Trans. on PAMI, vol. 29, no. 1, 2007.
  4. M . Lades , J . C . Vorbr ¨uggen , J . Buhmann, J. Lange, C. von der Malsburg , R . P . W¨urtz , and W . Konen, "Distortion invariant object recognition in the dynamic link architecture , " IEEE Trans. On Computers, vol. 42, pp. 300–311, 1993.
  5. Wiskott, J. -M. Fellous, N. Kr¨uger, and C. von der Malsburg, "Face recognition by elastic bunch graph matching," IEEE Trans. on PAMI, vol. 19, 1997.
  6. J. G. Daugman, "Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters" Journal of the Optical Society of America A, vol. 2, no. 7, pp. 1160–1169, 1985.
  7. T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, "Object recognition with cortex-like mechanisms," IEEE Trans. on PAMI, vol. 29, no. 3, 2007.
  8. W. Niblack et al. , "The QBIC Project," Proc. SPIE, vol. 1,908, pp. 173-181, Feb. 1993.
  9. J . G . Daugman, " Complete Discrete 2D Gabor Transforms by Neural Networks for Image Analysis and Compression," IEEE Trans.
  10. J. Ilonen, J. -K. Kamarainen, P. Paalanen, M. Hamouz, J. Kittler, and H. K¨alvi¨ainen, "Image feature localization by multiple hypothesis testing of Gabor features," IEEE Trans. on Image Processing, vol. 17, No. 3, pp. 311–325, 2008.
  11. J. Ilonen, J. -K. Kamarainen, and H. K¨alvi¨ainen, "Fast extraction of multi-resolution gabor features," in 14th Int Conf on Image Analysis and Processing (ICIAP), 2007, pp. 481–486.
  12. E. Simoncelli, W. Freeman, E. Adelson, and D. Heeger, "Shiftable Multiscale transforms," IEEE Transactions on Information Theory, vol. 38, no. 2, pp. 587–607, 1992.
  13. J . Sampo , J . -K . Kamarainen , M . Heilio , and H . Kalviainen "Measuring translation shiftability of frames , " Computers & Mathematics with Applications, vol. 52, no. 6-7, pp. 1089–1098, 2006.
  14. M. Hamouz, J. Kittler, J. -K. Kamarainen, P. Paalanen, H. Kalviainen, and J. Matas, "Feature-based affine-invariant localization of faces," IEEE Trans. on PAMI, vol. 27, no. 9, pp. 1490–1495, 2005.
  15. Cristianini , Nello and Shawe –Taylor , John, " An Introduction to Support Vector Machines and other kernel based learning methods", Cambridge University Press, Cambridge, 2000.
Index Terms

Computer Science
Information Sciences


Satellite Images Gabor filter feature vector DCT Support Vector Machine.