A novel oil spill detection method from synthetic aperture radar imageries via a bidimensional empirical mode decomposition

作者：Yang Yonghu; Li Ying^*; Zhu Xueyuan

来源：Acta Oceanologica Sinica, 2017, 36(7): 86-94.

DOI：10.1007/s13131-017-1086-z

• bidimensional empirical mode decomposition
• synthetic aperture radar image
• detection of oil spill
• hilbert spectral analysis

Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar (SAR) sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately.

• 出版日期2017-7
• 单位大连海事大学; 大连东软信息学院