文章基本信息

标题：Seabed Sediment Classification Using Spatial Statistical Characteristics
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作者：Zhang, Quanyin ; Zhao, Jianhu ; Li, Shaobo 等
期刊名称：Journal of Marine Science and Engineering
电子版ISSN：2077-1312
出版年度：2022
卷号：10
期号：5
页码：1-20
DOI：10.3390/jmse10050691
语种：English
出版社：MDPI AG
摘要：Conventional sediment classification methods based on Multibeam Echo System (MBES) data have low accuracy since the correlation between features and sediment has not been fully considered. Moreover, their poor resistance to the residual error of MBES backscatter strength (BS) processing also degrades their performances. Toward these problems, we propose a seabed sediment classification method using spatial statistical features extracted from angular response curve (ARC), topography, and geomorphology. First, to reduce interference of noise and residual error of beam pattern correction, we propose a robust method combining the Generic Seafloor Acoustic Backscatter (GSAB) model and Huber loss function to estimate the parameters of ARC which is strongly correlated with seabed sediments. Second, a feature set is constructed by AR features composed of GSAB parameters, BS mosaic and its derivatives, and seabed topography and its derivatives to characterize seabed sediments. After that, feature selection and probability map acquisition are employed based on the random forest algorithm (RF). Finally, a denoising and final sediment map generation method is proposed and applied to probability maps to obtain the sediment map with reasonable sediment distribution and clear boundaries between classes. We implement experiments and achieve the classification accuracy of 93.3%, which verifies the validity of our method.
关键词：acoustic sediment classification; angular response; probability map filter; random forest