Principal Component Analysis of Single-Beam Echo-Sounder Signal Features for Seafloor Classification

Abstract

Acoustic remote sensing is a useful tool for seafloor characterization. This contribution presents the results of seafloor sediment classification using single-beam echo-sounder (SBES) data based on a phenomenological method. Basic concepts of principal component analysis (PCA) and its applicability to the sediment classification using acoustical data are studied. This mathematical tool, which retains most of the variation of the data, is applied to the SBES echo shape parameters such as total energy, time-spread, skewness and flatness on three low (12 kHz), moderate (38 kHz), and high (200 kHz) frequencies, making 12 features in total. These parameters are dependent on sediment types and can therefore be used as attributes for seafloor classification. To decrease the statistical fluctuations of the extracted features, an averaging over a sufficiently large number of consecutive pings have been applied to the features. The SBES classification results based on the PCA and K-means clustering approach can clearly discriminate between different sediment classes. The signal at 12 kHz contains information on sediment layers (5 m depth). The performance of the method and the results obtained are assessed using the following independent criteria: 1) inspection of the track crossings indicates stable feature extraction and processing strategy; 2) comparison between the class numbers of the classification results and of the grab samples shows a significant correlation coefficient of 0.90; and 3) an error matrix verifies the stability and independence of the classification results from the features considered.