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SAR Image Classification by Wavelet Transform and Euclidean Distance with Shanon Index Measurement

A. Shakin Banu1 , P.Vasuki 2 , S. Mohamed Mansoor Roomi3 , A. Yusuf Khan4

Section:Research Paper, Product Type: Journal
Vol.6 , Issue.3 , pp.13-17, Jun-2018

Online published on Jun 30, 2018


Copyright © A. Shakin Banu, P.Vasuki, S. Mohamed Mansoor Roomi, A. Yusuf Khan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Citation :
IEEE Style Citation: A. Shakin Banu, P.Vasuki, S. Mohamed Mansoor Roomi, A. Yusuf Khan, “SAR Image Classification by Wavelet Transform and Euclidean Distance with Shanon Index Measurement”, International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.13-17, 2018.

MLA Style Citation: A. Shakin Banu, P.Vasuki, S. Mohamed Mansoor Roomi, A. Yusuf Khan "SAR Image Classification by Wavelet Transform and Euclidean Distance with Shanon Index Measurement." International Journal of Scientific Research in Network Security and Communication 6.3 (2018): 13-17.

APA Style Citation: A. Shakin Banu, P.Vasuki, S. Mohamed Mansoor Roomi, A. Yusuf Khan, (2018). SAR Image Classification by Wavelet Transform and Euclidean Distance with Shanon Index Measurement. International Journal of Scientific Research in Network Security and Communication, 6(3), 13-17.

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Abstract :
Synthetic Aperture Radar Image Classification is one of the four problem domains in the field of Automatic Target Recognition. This paper proposes Wavelet transform based Euclidean distance with Shanon Index measurement to classify the SAR image, which consists of three steps including preprocessing, feature extraction and classification. Preprocessing removes the speckle noise present in the image. Daubechies wavelet is used to extract the features by obtaining the approximated image. Finally, the classification process is completed using Euclidian distance with the help of Shannon index measurement. The performance of the existing system is compared with the existing Maximum Likelihood Classifier in terms of accuracy and an accuracy of 95.3% is achieved in classification for the proposed method.

Key-Words / Index Term :
SAR, image classification, feature extraction, Daubechies Wavelet, Euclidean distance and Shannon index measurement

References :
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