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.

References

31 Cites in Article

Reference Format

E Ngai,Yong Hu,Y Wong,Yijun Chen,Xin Sun (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature.
Wilson Rojas,Claudio Villegas (2013). Graphical Representation and Exploratory Visualization for Decision Trees in the KDD Process.
Borja Calvo,Pedro Larrañaga,José Lozano (2007). Learning Bayesian classifiers from positive and unlabeled examples.
G Yugal Kumar,Sahoo (2012). Analysisof Parametric & Non Parametric Classifiers for Classification Technique using WEKA, I.
Ismail Hmeidi,Bilal Hawashin,Eyas El-Qawasmeh (2008). Performance of KNN and SVM classifiers on full word Arabic articles.
Jing Hu,Daoliang Li,Yueqi Qinglingduan,Guifen Han,Xiuli Chen,Si (2012). Fish species classification by color, texture and multi-class support vector machine using computer vision.
Xiaowei Yang,Qiaozhen Yu,Lifang He,Tengjiao Guo (2013). The one-against-all partition based binary tree support vector machine algorithms for multi-class classification.
(2013). DOI reserve for Community Guide 048.
Yang Shao,Ross Lunetta (2012). Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points.
N Rajasekhar,S Babu,T Rajinikanth (2012). Magnetic resonance brain images classification using linear kernel based Support Vector Machine.
Yang Shao,Ross Lunetta (2012). Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points.
Hongji Lin,Han Lin,Weibin Chen (2011). Study on Recognition of Bird Species in Minjiang River Estuary Wetland.
Baoping Tang,Tao Song,Feng Li,Lei Deng (2014). Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine.
A Dileep,C Chandra Sekhar (2014). Class-specific GMM based intermediate matching kernel for classification of varying length patterns of long duration speech using support vector machines.
Hsun-Jung Cho,Ming-Te Tseng (2013). A support vector machine approach to CMOS-based radar signal processing for vehicle classification and speed estimation.
Hong Lam,Rajprasad Lee,Rajkumar,Hung Lai,Chin Lo,Dino Heng Wan,Isa (2013). Oil and gas pipeline failure prediction system using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach.
Elias Zintzaras,Axel Kowald (2010). Forest classification trees and forest support vector machines algorithms: Demonstration using microarray data.
Xinjun Peng,Yifei Wang,Dong Xu (2013). Structural twin parametric-margin support vector machine for binary classification.
Maysam Abedi,Gholam-Hossain Norouzi,Abbas Bahroudi (2012). Support vector machine for multi-classification of mineral prospectivity areas.
Miao Liu,Mingjun Wang,Jun Wang,Duo Li (2013). Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar.
F Löw,U Michel,S Dech,C Conrad (2013). Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines.
Steve Gunn (1998). Support Vector Machines for Classification and Regression.
Till Rumpf,Christoph Römer,Martin Weis,Markus Sökefeld,Roland Gerhards,Lutz Plümer (2012). Sequential support vector machine classification for small-grain weed species discrimination with special regard to Cirsium arvense and Galium aparine.
Hassiba Nemmour,Youcef Chibani (2006). Multiple support vector machines for land cover change detection: An application for mapping urban extensions.
Sujit Narwade,Mohit Kalra,Rajkumar Jagdish,Divya Varier,Sagar Satpute,Noor Khan,Gautam Talukdar,Vinod Mathur,Karthikeyan Vasudevan,Dinesh Pundir,Vishwas Chavan,Rajesh Sood (2011). Literature based species occurrence data of birds of northeast India.
D Lu,Q Weng (2007). A survey of image classification methods and techniques for improving classification performance.
S Jeyanthi (2007). An Independent Study Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Business Administration Marketing.
Team} Core (2013). R: A Language and Environment for Statistical Computing.
Jennifer Taylor,Alicia Lacovara,Gordon Smith,Ravi Pandian,Mark Lehto (2014). Near-miss narratives from the fire service: A Bayesian analysis.
David Rogers,Jonathan Suk,Jan Semenza (2014). Using global maps to predict the risk of dengue in Europe.
Rafael Pino-Mejías,María Cubiles-De-La-Vega,María Anaya-Romero,Antonio Pascual-Acosta,Antonio Jordán-López,Nicolás Bellinfante-Crocci (2010). Predicting the potential habitat of oaks with data mining models and the R system.

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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 Downloads: 2411

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