Supervised Classification of Remote Sensed Data using Support Vector Machine

Supervised Classification of Remote Sensed Data using Support Vector Machine

Tarun Rao PhD

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T.V.Rajinikanth

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Dayananda Sagar College of Engineering

Supervised Classification of Remote Sensed Data using Support Vector Machine

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Abstract

Support vector machines have been used as a classification method in various domains including and not restricted to species distribution and land cover detection. Support vector machines offer many key advantages like its capacity to handle huge feature spaces and its flexibility in selecting a similarity function. In this paper the support vector machine classification method is applied to remote sensed data. Two different formats of remote sensed data is considered for the same. The first format is a comma separated value format wherein a classification model is developed to predict whether a specific bird species belongs to Darjeeling area or any other region. The second format used is raster format which contains image of Andhra Pradesh state in India.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Tarun Rao. 2014. \u201cSupervised Classification of Remote Sensed Data using Support Vector Machine\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C1).

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GJCST Volume 14 Issue C1

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Classification Data mining remote sensed data support vector machine

Submission ReceivedDecember 8, 2013
Peer Review Double Blind
Handling Editor
Accepted January 1, 2014
Published January 15, 2014

Version of record

v1.2

Issue date

May 14, 2014

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Article Matrices

Total Score: 102

Country: Unknown

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Tarun Rao , T.V.Rajinikanth (PhD/Dr. count: 0)

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Total Views: 9108

Total Downloads: 2320

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