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Image Fusion and Fuzzy Clustering based Change Detection in SAR Images

Published on February 2015 by Hire Gayatri Ashok, D. R. Patil
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International Conference on Advances in Science and Technology
Foundation of Computer Science USA
ICAST2014 - Number 1
February 2015
Authors: Hire Gayatri Ashok, D. R. Patil
073d9c6c-314b-40e3-a46c-e78f46f65ad8

Hire Gayatri Ashok, D. R. Patil . Image Fusion and Fuzzy Clustering based Change Detection in SAR Images. International Conference on Advances in Science and Technology. ICAST2014, 1 (February 2015), 24-29.

@article{
author = { Hire Gayatri Ashok, D. R. Patil },
title = { Image Fusion and Fuzzy Clustering based Change Detection in SAR Images },
journal = { International Conference on Advances in Science and Technology },
issue_date = { February 2015 },
volume = { ICAST2014 },
number = { 1 },
month = { February },
year = { 2015 },
issn = 0975-8887,
pages = { 24-29 },
numpages = 6,
url = { /proceedings/icast2014/number1/19471-5011/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Science and Technology
%A Hire Gayatri Ashok
%A D. R. Patil
%T Image Fusion and Fuzzy Clustering based Change Detection in SAR Images
%J International Conference on Advances in Science and Technology
%@ 0975-8887
%V ICAST2014
%N 1
%P 24-29
%D 2015
%I International Journal of Computer Applications
Abstract

Change detection in remote sensing images becomes more and more important for the last few decades, among them change detection in Synthetic Aperture Radar (SAR) images are having some more difficulties than optical ones due to the fact that SAR images suffer from the presence of the speckle noise. This paper presents unsupervised change detection in multi-temporal Synthetic Aperture Radar (SAR) images based on Image Fusion and Fuzzy Clustering algorithms. Image fusion technique is used to generate difference image by collecting information from Log ratio image and Mean ratio image. In order to intensify the information of changed regions and suppress the background information, Contourlet fusion rules are chosen to fuse the contourlet coefficients. For classifying changed and unchanged regions a reformulated FLICM (Fuzzy Local Information c-means) is proposed. This method reduces the effect of speckle noise because it is insensitive to noise. Experimental results, obtained on real multi-temporal SAR images by the Reformulated FLICM clustering algorithm exhibited low error than pre-existence.

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Index Terms

Computer Science
Information Sciences

Keywords

Synthetic Aperture Radar(sar) Difference Image Image Fusion Image Change Detection Algorithms