Seabed classification using acoustic signals: A decision tree approach

Abstract

Decision trees are versatile machine learning algorithms that are frequently used in classification and regression tasks. In this work, decision trees are employed as tools for sediment classification using sound waves propagating in the ocean and their behavior and features. We first analyze the structure of received time series at deployed hydrophones, extracting characteristic features (kurtosis and skewness, for example). Feature values then form vectors that are used as input patterns to the trees. A training step is the first stage of the machine learning approach with the trees trained to recognize sediment types based on feature values. The method is subsequently tested on feature vectors obtained from noise-corrupted time series. The performance depends on Signal-to-Noise Ratio values as expected and the method is found to be superior to conventional machine learning approaches. The addition of tools such as principal component analysis as well as spectrogram processing and time-frequency curve fitting further enhances the method. The decision tree technique provides an effective and efficient solution to the problem of sediment classification using acoustic data. [Work supported by ONR.]