CFP last date
20 January 2025
Reseach Article

Applying Image Fusion and Fuzzy Clustering Algorithms to detect Changes in Synthetic Aperture Radar Images

by Deepthi Maryala, J.krishna Chaithanya, T. Ramashri
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 102 - Number 12
Year of Publication: 2014
Authors: Deepthi Maryala, J.krishna Chaithanya, T. Ramashri
10.5120/17871-8813

Deepthi Maryala, J.krishna Chaithanya, T. Ramashri . Applying Image Fusion and Fuzzy Clustering Algorithms to detect Changes in Synthetic Aperture Radar Images. International Journal of Computer Applications. 102, 12 ( September 2014), 38-43. DOI=10.5120/17871-8813

@article{ 10.5120/17871-8813,
author = { Deepthi Maryala, J.krishna Chaithanya, T. Ramashri },
title = { Applying Image Fusion and Fuzzy Clustering Algorithms to detect Changes in Synthetic Aperture Radar Images },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 102 },
number = { 12 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 38-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume102/number12/17871-8813/ },
doi = { 10.5120/17871-8813 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:32:58.513787+05:30
%A Deepthi Maryala
%A J.krishna Chaithanya
%A T. Ramashri
%T Applying Image Fusion and Fuzzy Clustering Algorithms to detect Changes in Synthetic Aperture Radar Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 102
%N 12
%P 38-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image change detection is a process in which two or more images of same scene that are captured at two different time instants are taken and changes are detected. Out of many categories of images, detection of changes in Synthetic Aperture Radar (SAR) images is a critical task because of the speckle noise present in these images. In this paper changes are detected in SAR images based on an image fusion strategy and fuzzy clustering algorithms. Image fusion is used to generate a difference image from the information obtained from mean ration and log ratio operator and Stationary Wavelet Transform (SWT) is used to decompose the sub bands and the two clustering algorithms namely fuzzy local information C means clustering (FLICM) algorithm and reformulated fuzzy local-information C-means clustering algorithm (RFLICM) are considered which are applied on the fused image and the changed and unchanged areas are differentiated by forming clusters of similar and dissimilar elements. Experiments are carried out on the different images by applying FLICM and RFLICM algorithms.

References
  1. Maoguo Gong, Zihqiang Zhou, Jingjing Ma " Change detection in Synthetic aperture Radar Images based on Image Fusion and Fuzzy Clustering", IEEE Trans on image processing , Vol 21, No:4, April 2012
  2. F. Bujor, E. Trouvé, L. Valet, J. M. Nicolas, and J. P. Rudant, "Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal SAR images," IEEE Trans. Geosci. Remote Sens. , vol. 42, no. 10, pp. 2073–2084, Oct. 2004.
  3. A. Robin, L. Moisan, and S. Le Hegarat-Mascle, "An a-contrario approach for subpixel change detection in satellite imagery," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 32, no. 11, pp. 1977–1993, Nov. 2010
  4. E. E. Kuruoglu and J. Zerubia, "Modeling SAR images with a generalization of the Rayleigh distribution," IEEE Trans. Image Process. , vol. 13, no. 4, pp. 527–533, Apr. 2004.
  5. F. Bovolo and L. Bruzzone, "A detail-preserving scale-driven approach to change detection in multitemporal SAR images," IEEE Trans. Geosci. Remote Sens. , vol. 43, no. 12, pp. 2963–2972, Dec. 2005.
  6. F. Chatelain, J. -Y. Tourneret, and J. Inglada, "Change detection in multisensor SAR images using bivariate Gamma distributions," IEEE Trans. Image Process. , vol. 17, no. 3, pp. 249–258, Mar. 2008.
  7. L. Bruzzone and D. F. Prieto, "An adaptive semiparametric and context- based approach to unsupervised change detection in multi-temporal remote-sensing images," IEEE Trans. Image Process. , vol. 11, no. 4, pp. 452–466, Apr. 2002.
  8. D. Rey, G. Subsol, H. Delingette, and N. Ayache, "Automatic detection and segmentation of evolving processes in 3-D medical images: Application to multiple sclerosis," Med. Image Anal. , vol. 6, no. 2, pp. 163–179, Jun. 2002.
  9. M. Bosc, F. Heitz, J. P. Armspach, I. Namer, D. Gounot, and L. Rumbach, "Automatic change detection in multimodal serial MRI: Application to multiple sclerosis lesion evolution," Neuroimage, vol. 20, no. 2, pp. 643–656, Oct. 2003
  10. S. Krinidis and V. Chatzis, "A robust fuzzy local information C-means clustering algorithm," IEEE Trans. Image Process. , vol. 19, no. 5, pp. 1328–1337, May 2010
  11. K. S. Tamilselvan and Dr. G. Murugesan "Automatic Tumour Detection in Brain Image using FLICM algorithm
  12. G. P Nason and B. W Silverman, "The Stationary wavelet transform and some statistical applications, Dept of Mathematics, UK.
  13. Bagawade Ramdas P,Bhagawat Keshav S, Patil Pradeep M, " Wavelet transform Techniques for Image Resolution Enhancement : A Study, ISSN 2250-2459, Volume 2, Issue 4, April 2012
  14. Y. Bazi, L. Bruzzone, and F. Melgani, "An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images," IEEE Trans. Geosci. Remote Sens. , vol. 43, no. 4, pp. 874–887, Apr. 2005.
  15. M. Ahmed, S. Yamany, N. Mohamed, A. Farag, and T. Moriarty, "A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data," IEEE Trans. Med. Imag. , vol. 21, no. 3, pp. 193–199, Mar. 2002
  16. W. Cai, S. Chen, and D. Zhang, "Fast and robust fuzzy C-means clustering algorithms incorporating local information for image segmentation," Pattern Recognit. , vol. 40, no. 3, pp. 825–838, Mar. 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Synthetic aperture radar (SAR) images Change Detection clustering Image Fusion fuzzy local information C- means algorithm (FLICM) reformulated fuzzy local information C- means algorithm (RFLICM)