Selecting Optimal RBF Kernel with Machine Learning for Feature Extraction and Classification in SAR Images

P. Deepthi Jordhana
P. Deepthi Jordhana * §
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K.Soundararajan
K.Soundararajan
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§ Intell Engineering College

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Selecting Optimal RBF Kernel with Machine Learning for Feature Extraction and Classification in SAR Images

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Abstract

Kernel methods are gaining popularity in image processing applications. The accuracy of feature extraction and classification on image data for a given application is greatly influenced by the choice of kernel function and its associated parameters. As on today there existing no formal methods for selecting the kernel parameters. The objective of the paper is to apply machine learning techniques to arrive at suitable kernel parameters and improvise the accuracy of kernel based object classification problem. The graph cut method with Radial Basis function (RBF) is employed for image segmentation, by energy minimization technique. The region parameters are extracted and applied to machine learning algorithm along with RBF’s parameters. The region is classified to be man made or natural by the algorithm. Upon each iteration using supervised learning method the kernel parameters are adjusted to improve accuracy of classification. Simulation results based on Matlab are verified for Manmade classification for different sets of Synthetic Aperture RADAR (SAR) Images.

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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

P. Deepthi Jordhana, K.Soundararajan. 2014. "Selecting Optimal RBF Kernel with Machine Learning for Feature Extraction and Classification in SAR Images". Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 14 (GJCST Volume 14 Issue F4).

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
graph cut kernelImage SegmentationMachine LearningRBF kernel
Classification
I.4.0
H.1.2
  • Submission ReceivedDecember 7, 2013
  • Peer Review Double Blind
    Handling Editor
  • Accepted December 31, 2013
  • Published January 15, 2014
Version of record

v1.2

Issue date
September 30, 2014

Language
English
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Selecting Optimal RBF Kernel with Machine Learning for Feature Extraction and Classification in SAR Images

P. Jordhana
P. Jordhana Intell Engineering College
K.Soundararajan
K.Soundararajan
  • Submission ReceivedDecember 7, 2013
  • Peer Review Double Blind
    Handling Editor
  • Accepted December 31, 2013
  • Published January 15, 2014