Multibeam acoustic seabed classification combining SVM and adaptive boosting algorithm

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 fine 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.