Abstract
In recent years, mankind has made great achievements in the marine exploration. Ocean contains abundant resources, and the seabed has recorded amount of basic Earth information. Therefore, a complete study of the seabed can help to form a full appreciation of underwater environment. The study of the seabed recognition method, as the most basic work of the study of the seabed, is gradually gaining the attention of researchers. As a main marine exploratory tool, the side-scan sonar is fast, accurate and convenient for seabed information collection. In this paper, lots of seabed acoustic images were applied to extract the seabed substrate characteristics using the gray covariance matrix method. An improved BP neural network model was involved into classify and identify the seabed characteristics. In addition, several algorithms for BP neural network were proposed for testing the recognition accuracy of side-scan acoustic images and the convergence rate. The results show that although several algorithms were easy to fall into the minimum value during training, which can lead to slow convergence rate and unable to meet the recognition accuracy standard, the trainlm function had a faster convergence rate and higher recognition accuracy.
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Acknowledgment
This research work is supported by Major National Science and Technology Project (2015ZX01041101), and the National Natural Science Foundation of China (51609050, 51409059, 51509057).
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Xi, H., Wan, L., Sheng, M., Li, Y., Liu, T. (2017). The Study of the Seabed Side-Scan Acoustic Images Recognition Using BP Neural Network. In: Chen, G., Shen, H., Chen, M. (eds) Parallel Architecture, Algorithm and Programming. PAAP 2017. Communications in Computer and Information Science, vol 729. Springer, Singapore. https://doi.org/10.1007/978-981-10-6442-5_12
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DOI: https://doi.org/10.1007/978-981-10-6442-5_12
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