Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image

Abstract

[1] Polarimetric SAR decomposition parameters, average alpha angle ( ) and entropy (H) are estimated for oil-slick contaminated sea surfaces and slick-free conditions using a RADARSAT-2 quad-polarization SAR image. The values of H and within oil slick areas are significantly higher than those of the ambient sea surface, indicating the dominance of Bragg scattering for the slick-free ocean and non-Bragg scattering for the oil-slick area. In land classification, the conformity coefficient (m) is often used to discriminate surface scattering with double-bounce or volume scattering. Based on this rationale, we also develop a method using m as a logical scalar descriptor to map oil slicks under low-tomoderate wind conditions. The proposed method is assessed using a RADARSAT-2 quad-polarization SAR image of oil slicks in the Gulf of Mexico. Analysis shows that when m is positive the sea surface is slick-free, whereas m is negative for oil-slick areas. This method provides a simple and effective mapping technique for oil slick detection.

Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network

“…The measurement retains all the information describing the polarimetric properties of the observed scene in the scattered field. Due to the vector nature of the scattered field, quad-polarization measurements can be used to classify SAR imagery with different scattering mechanisms [8,15]. However, previously, most research on the identifying of ocean oil spill from the water was based only on a single fully Pol-SAR feature, which would inevitably limit the enhancement of classification accuracy.…”

“…The combined usage of the multiple fully Pol-SAR feature images are necessary to help improve the classification performance of oil spills and water. As a general view, the chosen fully Pol-SAR features are effective for oil spill [8]; ρ: co-polarized complex correlation [7]; H: polarization entropy [1]; A: anisotropy coefficient [25]; α: mean scattering angle [25]; µ: conformity coefficient [15]; P: degree of polarization [22]. …”

“…It is not necessary for every single Pol-SAR feature to be used in the classification process. The commonly used fully Pol-SAR features for oil spill detection include span [8], ρ [7], H [1], A [25], α [25], µ [15], and P [22]. Each of these features has its own different ability for identifying the ocean oil spills from water.…”

“…Numbers of hidden layer nodes are determined by the testing. In this study, we set the number of the hidden layer nodes as 15,20,25,30,35, and 40 to evaluate the convergence and classification performance under different neural network structure. The numbers of output layer nodes is equal to the number of classified types.…”

“…Efforts have been devoted to improve oil spill detection and classification. Nowadays, there is a general consensus that the extra information provided by the polarimetric SAR (Pol-SAR) data enhances the capabilities of identifying and classifying the A 12 High Low Skrunes, Brekke, and Eltoft (2014) [8] α Low High Minchew, Jones, and Holt (2012) [25] ν High Low Skrunes, Brekke, and Eltoft (2014) [8] [15], Skrunes Brekke, and Eltoft (2014) [8] Over the past several decades, artificial neural network algorithms have been widely used in remote sensing image classification [27][28][29], due to their good self-organization [30][31][32], self-learning [33,34], and self-adaptive abilities [35]. Among these, BP (Back Propagation) neural network is one of the first widely used network models due to its simple and easy to implement training in initially stage.…”