基于Gammatone滤波器组时频谱和卷积神经网络的海底底质分类

Seabed sediment classification based on gammatone filter banks time-frequency spectrum and convolutional neural networks

为了有效利用海底底质信号完成海底底质的分类识别,该文提出一种将深度学习方法和底质信号相结合实现底质分类识别的方法。首先利用Gammatone滤波器组计算底质侧扫图像信号的时频谱,然后通过卷积神经网络对得到的时频谱进行分类识别完成底质分类。利用加利福尼亚州Scott Creek近海采集的侧扫声呐图像数据进行数据分析,结果表明应用该方法的底质分类准确率平均达到99.15%,相对于利用分类器分类人工提取的底质分类特征,分类性能更加优越;同时利用该方法处理海上试验数据,结果证明该方法具有一定的泛化能力。该文研究结果对实际的海底底质分类具有一定参考意义。

In order to effectively use sea bottom sediment signal to accomplish the classification and recognition of the sediments,a method of combining the deep learning and the sediment signal to achieve the classification and identification of the sea bottom sediment is proposed in this paper.First,the Gammatone filter banks is used to calculate the time-frequency spectrum of sediments side scan sonar image signals.In the end,using a CNN model to classify the time-frequency spectrum calculated by Gammatone filter banks.The results of data analysis with side scan sonar image data collected offshore of Scott Creek,California show that the classification and recognition accuracy of sediments by this method can averagely reach 99.15%,which is superior to using classifiers to classify sediment classification features manually extracted in classification performance,and the results of using this method to process the sea trial data show the means proposed by this paper has a certain generalization ability.The results of this study have specific reference significance for actual seabed sediments classification.