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A Survey of Satellite High Resolution Image Classification

by Ruby Bharti, Jitendra Kurmi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 164 - Number 1
Year of Publication: 2017
Authors: Ruby Bharti, Jitendra Kurmi
10.5120/ijca2017913566

Ruby Bharti, Jitendra Kurmi . A Survey of Satellite High Resolution Image Classification. International Journal of Computer Applications. 164, 1 ( Apr 2017), 26-28. DOI=10.5120/ijca2017913566

@article{ 10.5120/ijca2017913566,
author = { Ruby Bharti, Jitendra Kurmi },
title = { A Survey of Satellite High Resolution Image Classification },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 164 },
number = { 1 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 26-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume164/number1/27448-2017913566/ },
doi = { 10.5120/ijca2017913566 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:11:07.283897+05:30
%A Ruby Bharti
%A Jitendra Kurmi
%T A Survey of Satellite High Resolution Image Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 164
%N 1
%P 26-28
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The aim of this research is to present a detailed step-by-step method for classification of very high resolution urban satellite images (VHRSI) into specific classes such as road, building, vegetation, etc., using fuzzy logic. In this study, object-based image analysis is used for image classification. The main problems in high resolution image classification are the uncertainties in the position of object borders in satellite images and also multiplex resemblance of the segments to different classes. In order to solve this problem, fuzzy logic is used for image classification, since it provides the possibility of image analysis using multiple parameters without requiring inclusion of certain thresholds in the class assignment process Image classification is the most important process in pattern reorganization. Image classification is one of the most complex areas in image processing. It is more complex and difficult to classify if it contain blurry and noisy content. There are several methods to classify images and they provide good classification result but they fail to provide satisfactory classification result when the image contains blurry and noisy content. The two main methods for image classification are supervised and unsupervised classification. In general, this is a final stage of pattern matching. The classification process described the percentage of accuracy in pattern recognition. Feature extraction is another vital stage in pattern matching. These extracted feature are used for classification of the image database, that is pattern matching.

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Index Terms

Computer Science
Information Sciences

Keywords

Classification Pattern recognition fuzzy rule based systems very high resolution satellite imagery.

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