关键词: 多波束声纳高程数据, 人工鱼礁, 卷积神经网络, BP神经网络

Abstract: In order to establish an accurate multibeam sonar elevation data prediction model and solve the problem of the accuracy of air-squared prediction of artificial reefs,a multibeam sonar elevation data prediction method based on a combined model of improved convolutional neural network(CNN) and BP neural network is proposed.First,the improved CNN is used to extract topographic trend features by full convolutional operation of the elevation data,and then input to BP to further explore the internal topographic trend change pattern,so as to achieve the prediction of multibeam sonar elevation data.Experiments are conducted with multibeam sonar elevation data from a submarine ranch and cross-validated using the null square volume of artificial reefs.Finally,it is compared with the traditional kriging,BP,GA-BP,and PSO-BP models.The results show that the improved CNN-BP model performs the best prediction results on multibeam sonar elevation data and artificial reef air-square volume,which verifies the feasibility,reliability and high accuracy of the proposed method.

Key words: Multibeam sonar elevation data, Artificial reef, Convolutional neural network, BP neural network