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Oil Spill Detection from Sar Image Using Svm Based Classification : Volume Xl-1/W3, Issue 1 (24/09/2013)

By Matkan, A. A.

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Book Id: WPLBN0004014531
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Oil Spill Detection from Sar Image Using Svm Based Classification : Volume Xl-1/W3, Issue 1 (24/09/2013)  
Author: Matkan, A. A.
Volume: Vol. XL-1/W3, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection, Copernicus Publications
Historic
Publication Date:
2013
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications

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Hajeb, M., Azarakhsh, Z., & Matkan, A. A. (2013). Oil Spill Detection from Sar Image Using Svm Based Classification : Volume Xl-1/W3, Issue 1 (24/09/2013). Retrieved from http://community.worldlibrary.net/


Description
Description: Remote Sensing & GIS Department, Shahid Beheshti University, Evin, Tehran, Iran. In this paper, the potential of fully polarimetric L-band SAR data for detecting sea oil spills is investigated using polarimetric decompositions and texture analysis based on SVM classifier. First, power and magnitude measurements of HH and VV polarization modes and, Pauli, Freeman and Krogager decompositions are computed and applied in SVM classifier. Texture analysis is used for identification using SVM method. The texture features i.e. Mean, Variance, Contrast and Dissimilarity from them are then extracted. Experiments are conducted on full polarimetric SAR data acquired from PALSAR sensor of ALOS satellite on August 25, 2006. An accuracy assessment indicated overall accuracy of 78.92% and 96.46% for the power measurement of the VV polarization and the Krogager decomposition respectively in first step. But by use of texture analysis the results are improved to 96.44% and 96.65% quality for mean of power and magnitude measurements of HH and VV polarizations and the Krogager decomposition. Results show that the Krogager polarimetric decomposition method has the satisfying result for detection of sea oil spill on the sea surface and the texture analysis presents the good results.

Summary
Oil spill detection from SAR image using SVM based classification

 

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