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Multibeam acoustic seabed classification combining SVM and adaptive boosting algorithm
Acta Geodaetica et Cartographica Sinica
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 withindoi:10.11947/j.agcs.2021.20200556 doaj:c9850562c7254befa40c018529b489af fatcat:2tr4zkxifzg75du32wmj366rjm