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Classification of Satellite Images through Gabor Filter using SVM

by Manali Jain, Amit Sinha
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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
10.5120/20348-2534

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 = { https://ijcaonline.org/archives/volume116/number7/20348-2534/ },
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
Abstract

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.

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Index Terms

Computer Science
Information Sciences

Keywords

Satellite Images Gabor filter feature vector DCT Support Vector Machine.

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