Back propagation neural network classification of sediment seabed acoustic sonar images based on particle swarm optimization algorithms

Chen Jiabing; Wu Ziyin; Zhao Dineng; Zhou Jieqiong; Li Shoujun; Shang Jihong

doi:10.3969/j.issn.0253-4193.2017.09.005

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Chen Jiabing, Wu Ziyin, Zhao Dineng, Zhou Jieqiong, Li Shoujun, Shang Jihong. Back propagation neural network classification of sediment seabed acoustic sonar images based on particle swarm optimization algorithms[J]. Haiyang Xuebao, 2017, 39(9): 51-57. doi: 10.3969/j.issn.0253-4193.2017.09.005

Citation:

Chen Jiabing, Wu Ziyin, Zhao Dineng, Zhou Jieqiong, Li Shoujun, Shang Jihong. Back propagation neural network classification of sediment seabed acoustic sonar images based on particle swarm optimization algorithms[J]. Haiyang Xuebao, 2017, 39(9): 51-57. doi: 10.3969/j.issn.0253-4193.2017.09.005

Citation:

Chen Jiabing, Wu Ziyin, Zhao Dineng, Zhou Jieqiong, Li Shoujun, Shang Jihong. Back propagation neural network classification of sediment seabed acoustic sonar images based on particle swarm optimization algorithms[J]. Haiyang Xuebao, 2017, 39(9): 51-57. doi: 10.3969/j.issn.0253-4193.2017.09.005

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Back propagation neural network classification of sediment seabed acoustic sonar images based on particle swarm optimization algorithms

doi: 10.3969/j.issn.0253-4193.2017.09.005

1.
Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China;Key Laboratory of Submarine Geosciences, State Oceanic Administration, Hangzhou 310012, China
2.
Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China;Department of Earth Science, Zhejiang University, Hangzhou 310027, China

Received Date: 2016-10-15

Abstract

Abstract

By combining Particle Swarm Optimization (PSO) with BP neural network, the initial weights and thresholds of BP neural network classification are optimized by utilizing PSO with strong robustness and global searching ability. Extracting six main feature vectors of sandy, rocks and mud in the seabed sonar images based on the data of side scan sonar in the Zhujiang Estuary Delta, using the PSO-BP method to classify seabed sediment. The experiment shows that the accuracy of the sediments classification is more than 90%, higher than the accuracy about 70% which using BP neural network only. It proves that the PSO-BP method can be effectively applied to the identification and classification of sediment seabed.
- PSO-BP neural network,
- feature vectors,
- particle swarm optimization algorithms,
- sediment classification

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References(26)

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