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Implementation of Image Registration for Satellite Images using Mutual Information and Particle Swarm Optimization Techniques

by Heena R. Kher
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 97 - Number 1
Year of Publication: 2014
Authors: Heena R. Kher
10.5120/16969-5475

Heena R. Kher . Implementation of Image Registration for Satellite Images using Mutual Information and Particle Swarm Optimization Techniques. International Journal of Computer Applications. 97, 1 ( July 2014), 7-14. DOI=10.5120/16969-5475

@article{ 10.5120/16969-5475,
author = { Heena R. Kher },
title = { Implementation of Image Registration for Satellite Images using Mutual Information and Particle Swarm Optimization Techniques },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 97 },
number = { 1 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 7-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume97/number1/16969-5475/ },
doi = { 10.5120/16969-5475 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:22:57.601995+05:30
%A Heena R. Kher
%T Implementation of Image Registration for Satellite Images using Mutual Information and Particle Swarm Optimization Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 97
%N 1
%P 7-14
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The aim of this research is to register satellite images on the DSP processor using probabilistic optimization method named as particle swarm optimization. Satellite image registration is necessary in order to find change detection, to eliminate influence of camera distortion (roll, pitch and yaw), merge satellite imagery and in urban planning. Particle Swarm Optimization is a stochastic search technique with less computation and still very effective as compared to other optimization techniques. It is based on bird flocking, fish schooling and swarm theory. Each particle changes its position and velocity based on its corresponding fitness value. Fitness value can be calculated using joint entropy and mutual information. The algorithm can be used in object recognition, image segmentation, matching and registration. The performance of this algorithm is measured and results are shown using DSK 6713 hardware along with VM32242.

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Index Terms

Computer Science
Information Sciences

Keywords

Image Registration Mutual Information Joint Histogram Joint Entropy Particle Swarm Optimization

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