Self-adaptive Entropy Weighted Decision Fusion Method for Ship Image Classification Based on Multi-scale Convolutional Neural Network

Yongmei REN; Jie YANG; Zhiqiang GUO; Hui CAO

doi:10.11999/JEIT200102

Volume 43 Issue 5

May 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(5): 1424-1431

Yongmei REN, Jie YANG, Zhiqiang GUO, Hui CAO. Self-adaptive Entropy Weighted Decision Fusion Method for Ship Image Classification Based on Multi-scale Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1424-1431. doi: 10.11999/JEIT200102

Citation:

Yongmei REN, Jie YANG, Zhiqiang GUO, Hui CAO. Self-adaptive Entropy Weighted Decision Fusion Method for Ship Image Classification Based on Multi-scale Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1424-1431. doi: 10.11999/JEIT200102

Citation:

Yongmei REN, Jie YANG, Zhiqiang GUO, Hui CAO. Self-adaptive Entropy Weighted Decision Fusion Method for Ship Image Classification Based on Multi-scale Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1424-1431. doi: 10.11999/JEIT200102

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Self-adaptive Entropy Weighted Decision Fusion Method for Ship Image Classification Based on Multi-scale Convolutional Neural Network

doi: 10.11999/JEIT200102

Yongmei REN^{1, 2},
Jie YANG^{1
,
,},
Zhiqiang GUO¹,
Hui CAO¹

1.
Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
2.
School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang 421002, China

Funds: The National Natural Science Foundation of China (51879211), The National Key Research and Development Program of China (2020YFB1710800), The Scientific Research Project of the Hunan Provincial Education Department (18C0900)

Received Date: 2020-02-11
Rev Recd Date: 2020-10-28

Available Online: 2020-11-16

Publish Date: 2021-05-18

Abstract

Abstract

Considering the limitation of single scale Convolutional Neural Network (CNN) for ship image classification, a self-adaptive entropy weighted decision fusion method for ship image classification based on multi-scale CNN is proposed. Firstly, the multi-scale CNN is used to extract the multi-scale features of ship image with different sizes, and the optimum models of different sub-networks are trained. Then, the ship images of test set are tested on the optimum models, and the probability value that is output by Softmax function of multi-scale CNN is obtained, which is used to calculate the information entropy so as to realize the adaptive weight assigned to different input ship images. Finally, self-adaptive entropy weighted decision fusion is carried out for the probability value that is output by Softmax function of different sub-networks to realize the final ship image classification. Experiments perform on VAIS (Visible And Infrared Spectrums) and self-built datasets respectively, and the proposed method achieves average accuracy of 95.07% and 97.50% on these datasets respectively. The experimental results show that the proposed method has better classification performance than those of the single scale CNN classification method and other state-of-the-art methods.
- Image processing,
- Ship image classification,
- Multi-scale Convolutional Neural Network (CNN),
- Entropy,
- Decision fusion

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References(22)

References

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