Abstract
Abstract. 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.
Highlights
In the recent years, oil spill in marine environments is one of the most important factors of marine pollution that has very harmful impact on marine ecosystems, economy and human life
The algorithm is applied on the fully polarimetric L-band SAR data and the results following: Magnitude and power measurements derived from HH and VV polarizations are demonstrated in figure 3a, 3b, 3c and 3d respectively
In this research a method for oil spill detection from full polarimetric SAR data is presented that it is based on SVM classification
Summary
Oil spill in marine environments is one of the most important factors of marine pollution that has very harmful impact on marine ecosystems, economy and human life. Oil spills on SAR images damp short gravity-capillary waves and decrease backscatter of sea surface resulting in creating dark formations on SAR images having contrast with surrounding sea surface (Holt, 2004;Brekke and Solberg, 2005; Migliaccio et al, 2007; Fingas and Brown, 2011). This characteristic leads to identification of the spills on SAR images. In this study Support Vector Machine classifier is used for classification
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