Sunken oil detection and classification using MBES backscatter data

Sunken oil incidents have occurred multiple times in the Bohai Sea over the past ten years. Currently, quick and effective sunken oil detection and classification remains a difficult problem. In this study, sonar detection experiments are conducted to obtain acoustic image samples using a multibeam echosounder (MBES) in a large seawater tank at the bottom of the area where the sunken oil is located. A series of MBES data corrections are constructed to generate backscatter strength images that can reflect the target characteristics directly. Meanwhile, eight-dimensional features are extracted, and a support vector machine (SVM) classification framework is built to classify the sunken oil and other interference targets. The results indicate that the MBES backscatter images provide an alternative approach for detecting and classifying sunken oil. The overall target classification accuracy reaches 88.5% by the SVM algorithm. Thus, this study provides a basis for further investigation of detecting sunken oil.