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

Abstract

Oil spill accidents from ship or oil platform cause damage to marine and coastal environment and ecosystems. To monitor such spill events from space, fully polarimetric (Pol-SAR) synthetic aperture radar (SAR) has been greatly used in improving oil spill observation. Aiming to promote ocean oil spill classification accuracy, we developed a new oil spill identification method by combining multiple fully polarimetric SAR features data with an optimized wavelet neural network classifier (WNN). Two sets of RADARSAT-2 fully polarimetric SAR data are applied to test the validity of the developed method. The experimental results show that: (1) the convergence ability of optimized WNN can be enhanced, improving overall classification accuracy of ocean oil spill, in comparison to the classification results based on a common un-optimized WNN classifier; and (2) the joint use of the multiple fully Pol-SAR features as the inputs of the classifier can achieve better classification result than that only with single fully Pol-SAR feature. The developed method can improve classification accuracy by 4.96% and 7.75%, compared with the classification results with un-optimized WNN and only with one single fully polarimetric SAR feature. The classification overall accuracy based on the proposed approach can reach 97.67%. Experimental results have proven that the proposed approach is effective and applicable to classify the ocean oil spill.

Exploratory Data Analysis of Synthetic Aperture Radar (SAR) Measurements to Distinguish the Sea Surface Expressions of Naturally-Occurring Oil Seeps from Human-Related Oil Spills in Campeche Bay (Gulf of Mexico)

“…It also paves the way for full polarimetric investigations that could indeed search other ways to differentiate oil seeps from oil spills using SAR measurements (e.g., [74,75]). …”

Multi-Feature Based Ocean Oil Spill Detection for Polarimetric SAR Data Using Random Forest and the Self-Similarity Parameter

“…Ramsey et al used Freeman-Durden decomposition and Cloude-Pottier decomposition combined with Wishart classification for oil spill detection [15], etc. In consideration of the complementarity among features to the classification performance, it may be possible to use multi-features simultaneously to improve the oil spill detection accuracy and reduce the false alarm caused by look-alikes [11,27]. Table 1.…”

“…Compared with single-polarization SAR, polarimetric SAR includes not only the power information but also the relative phase information between channels, which can reflect the difference in target characteristics. Many studies have proven that the extra polarimetric information contained in polarimetric SAR data can provide powerful discrimination between oil slicks and look-alikes [3,[10][11][12]].…”

Retrieval of Oil–Water Mixture Ratio at Ocean Surface Using Compact Polarimetry Synthetic Aperture Radar

“…Thus, QP SAR systems provide optimal observations and the complete polarization scattering properties of the ocean surface scatterers and targets. QP SAR data have been applied in many studies related to oil spill detection and monitoring in the marine environment [1][2][3][4][5][6][7][8]. For a clean ocean surface with medium incidence angle (20 • -60 • ) and wind speed 3-14 m/s (which is necessary for slick detection by SAR), the main scattering mechanism is Bragg scattering.…”