Acoustic Deep-Sea Seafloor Characterization Accounting for Heterogeneity Effect

Abstract

An algorithm is described and tested to provide accurate and robust deep-sea seafloor classification based on the backscatter data derived from a multibeam bathymetry system. This article focuses on significant heterogeneity in the deep-sea backscatter strength (BS) data. The angular response curve information is decomposed into different units, and BS data are grouped on the basis of the incidence angle to address the heterogeneity in the across-ship direction. Subsequently, a sliding window is applied on BS data in each group, and a robust estimation method is used to address the potential heterogeneity in the window during feature extraction. Thereafter, the extracted features are learned by fuzzy c-means (FCM) to obtain a clustering solution. In the learning process, the features of each group are learned by an independent FCM. The modified FCM algorithm is used to cluster each group of data to handle unbalanced backscatter data sets. With this procedure, heterogeneity in BS data can be accounted for, which is universal in deep-sea survey application. Finally, the results of the different groups are merged to obtain a global label set for the survey region. The method was tested on the multibeam data collected from an offshore region around the Kyushu–Palau Ridge. Monte Carlo simulation was performed to evaluate the performance of the robust method. Computational results demonstrate that the improved algorithm can address the heterogeneity in BS data efficiently and provide an accurate classification solution in the deep-sea survey environment.