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Remote Sensing Image Matching using Sift And Affine Transformation

by Elsa Kuriakose, Anjaly Viswan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Number 14
Year of Publication: 2013
Authors: Elsa Kuriakose, Anjaly Viswan
10.5120/13930-1896

Elsa Kuriakose, Anjaly Viswan . Remote Sensing Image Matching using Sift And Affine Transformation. International Journal of Computer Applications. 80, 14 ( October 2013), 22-27. DOI=10.5120/13930-1896

@article{ 10.5120/13930-1896,
author = { Elsa Kuriakose, Anjaly Viswan },
title = { Remote Sensing Image Matching using Sift And Affine Transformation },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 80 },
number = { 14 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume80/number14/13930-1896/ },
doi = { 10.5120/13930-1896 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:54:33.442786+05:30
%A Elsa Kuriakose
%A Anjaly Viswan
%T Remote Sensing Image Matching using Sift And Affine Transformation
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 14
%P 22-27
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents remote sensing image matching using sift algorithm and affine transformation. The novelty in our approach is to find the features in the reference image and then match the input image with that of reference image using Affine Transformation. Both synthetic and real data have been considered in this work for the evaluation of the proposed methodology. After registering the image, the outliers are removed. A speeded up affine invariant detector is proposed in this paper for local feature extraction. The experimental results show that the proposed algorithm decreases the redundancy of key points and speeds up the implementation. It is able to account for differences in spectral content, rotation, scale, translation, different viewpoint, and change in illumination. The proposed technique improves the computational efficiency and decrease the storage requirement.

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Index Terms

Computer Science
Information Sciences

Keywords

Image segmentation scale invariant feature transform affine transformation remote sensing .

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