Chen, J. and Zhang, S., 2018. Segmentation of sonar image on seafloor sediments based on multiclass SVM. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 597–602. Coconut Creek (Florida), ISSN 0749-0208.
Because the seabed sediment sonar image has the complex background and low noise-signal ratio, the existing image segmentation methods are difficult to extract different types of sonar image features from the complex background of seabed sediment sonar image. The image segmentation accuracy is low. Thus, this paper presents a method for segmenting seabed sediment sonar image based on multiclass SVM. Firstly, the seabed sediment sonar image was denoised to extract the statistical feature, texture feature and gray feature with phase consistency. Then they were composed into characteristic sample vectors of multidimensional seabed sediment sonar image. Finally, SVM classification method was used to segment the seabed sediment sonar image. Experimental result shows that the proposed method can obtain the result with higher recognition rate.