任永梅^{1, 2},
杨杰^1,,,
郭志强¹,
曹辉¹
1.武汉理工大学信息工程学院宽带无线通信与传感器网络湖北省重点实验室 武汉 430070
2.湖南工学院电气与信息工程学院 衡阳 421002
基金项目:国家自然科学基金 (51879211)，国家重点研发计划(2020YFB1710800)，湖南省教育厅科学研究项目(18C0900)

详细信息

作者简介:任永梅：女，1988年生，博士生，研究方向为图像处理与模式识别
杨杰：女，1960年生，教授，研究方向为图像处理与模式识别
郭志强：男，1976年生，教授，研究方向为图像处理与模式识别
曹辉：男，1986年生，讲师，研究方向为数字信号的智能处理

通讯作者:杨杰　jieyang@whut.edu.cn

中图分类号:TN911.73; TP391.4

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出版历程

收稿日期:2020-02-11
修回日期:2020-10-28
网络出版日期:2020-11-16
刊出日期:2021-05-18

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

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)


摘要
摘要:针对单一尺度卷积神经网络(CNN)对船舶图像分类的局限性，该文提出一种多尺度CNN自适应熵加权决策融合方法用于船舶图像分类。首先使用多尺度CNN提取不同尺寸的船舶图像的多尺度特征，并训练得到不同子网络的最优模型；接着利用测试集船舶图像在最优模型上测试，得到多尺度CNN的Softmax函数输出的概率值，并计算得到信息熵，进而实现对不同输入船舶图像赋予自适应的融合权重；最后对不同子网络的Softmax函数输出概率值进行自适应熵加权决策融合实现船舶图像的最终分类。在VAIS数据集和自建数据集上分别进行了实验，提出的方法的分类准确率分别达到了95.07%和97.50%，实验结果表明，与单一尺度CNN分类方法以及其他较新方法相比，所提方法具有更优的分类性能。
关键词:图像处理/
船舶图像分类/
多尺度卷积神经网络/
熵/
决策融合
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.
Key words:Image processing/
Ship image classification/
Multi-scale Convolutional Neural Network (CNN)/
Entropy/
Decision fusion



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