Pol-SAR Based Oil Spillage Classification With Various Scenarios of Prior Knowledge

Oil slicks from ships or oil platforms cause serious damage to the marine and coastal environment and ecosystems. To monitor such spill events, fully polarimetric (Pol-SAR) synthetic aperture radar (SAR) has been widely used in improving oil spillage detection performance of the image segmentation methods and machine learning methods. A key factor that greatly affects the performance of the existing methods is the use of prior knowledge, such as the manual labels of oil slicks and clean seawater. In some scenarios, a large number of trustworthy manual labels of oil slicks have been accumulated. In other scenarios, however, there are few manual labels. To efficiently utilize the manual labels in various scenarios, we propose an oil spillage identification method, named RampOC. We design cost-sensitive risk minimization model to tackle the problem of imbalance of oil slicks and seawater, employ the ramp loss function to deal with various cases of the manual labels, and design a kernel-based CCCP algorithm to train a classifier by solving a series of convex dual programming. The trained classifier is applicable to various cases, including the normal case that a certain number of the labels of oil slicks are labeled, the case that a large proportion of labels have gone missing or incorrect, and the case that no labels of oil slicks are known. In all of these cases, RampOC has proven to be effective to detect the ocean oil spillage regions. All the MATLAB source codes could be freely downloaded from https://github.com/Isaac-QiXing/rampOC.