As a new technology, multibeam acoustic classification has been rapidly developed in recent years. A seabed sediment classification approach, GA-SVM-AdaBoost algorithm, is proposed by using the genetic algorithm (GA) optimized support vector machines (SVM) classifier as the AdaBoost weak classifier to solve the multi-classification problem in multibeam acoustic seabed classification. The sonar mosaic is obtained from multibeam echo sounder backscatter data collected in the Jiaozhou Bay within

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... ne processing. The 10 dimensions advantage features are selected by SVM-RFE-CBR algorithm before input GA-SVM-AdaBoost classification model. Compared with SVM, GA-SVM and AdaBoost based on single-layer decision tree, the classification results of GA-SVM-AdaBoost algorithm are more satisfactory. The total classification accuracy is as high as 92.19%, which is better than the other three models. It is proved that the proposed method can be effectively applied to high precision seabed sediment identification.