The accurate acquisition of seabed surface sediment distribution information plays an important role in the construction of marine basic geographical database.At present,multi beam is one of the effective means to achieve a wide range of seabed sediment classification.Acoustic features derived from multi-beam bathymetry and backscatter intensity data are widely used in sediment classification modeling.However,as the feature dimension increases,the presence of irrelevant and redundant features in the feature space seriously affects the accuracy of sediment classification.In order to quantitatively evaluate the representation ability of acoustic features on sediment categories and eliminate the interference of invalid features on classification results,this paper proposes a seabed sediment classification method based on multi-dimensional acoustic feature optimization.Firstly,based on the physical properties of actual sediment samples,multidimensional features are sorted and optimized to eliminate redundant and irrelevant features.Secondly,support vector machine,random forest and depth belief network are respectively applied to construct the seabed sediment supervision classification model.Through experiments using multi beam survey data and field sampling information from the southern part of Ireland,the results showed that the proposed method achieved the highest overall classification accuracy and Kappa coefficient of 86.20%and 0.834%for seabed sediment,respectively.Compared with principal component analysis and entropy index feature selection methods,it has significantly improved,highlighting the potential application of this method in seabed sediment detection and mapping.
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