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Underwater Acoustic Feature Extraction Based on Bidimensional Empirical Mode Decomposition in Shadow Field

机译:基于二维经验模态分解的水下声特征提取

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Recent developments in feature extraction based on Bidimensional Empirical Mode Decomposition (BEMD)is mainly about the optical image. Here we applied the BEMD to the underwater acoustic image feature extraction.Acoustic shadow fields always disturb feature extraction.To reduce it, we decomposed the underwater acoustic image into several Intrinsic Mode Functions (IMFs) and a residue.Thus, the Canny edge detector could ex,act better features from the first IMF. Sometimes, it is necessary to detect the sum of the first two IMFs. Experiments prove that this novel method really enhance the features of objects (physiognomy and texture) in the acoustic shadow field and weaken the edge of the acoustic shadow field.
机译:基于二维经验模态分解(BEMD)的特征提取技术的最新发展主要是关于光学图像。这里我们将BEMD应用于水下声像特征提取中,声影场总是会干扰特征提取,为减少声影场,我们将水下声像分解为几个本征函数(IMF)和残差,因此Canny边缘检测器可以例如,第一个IMF发挥更好的功能。有时,有必要检测前两个IMF的总和。实验证明,这种新颖的方法确实增强了声影场中物体的特征(相貌和纹理),并削弱了声影场的边缘。

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