基于多波段深度神经网络的舰船目标识别

刘峰; 沈同圣; 马新星; 张健

doi:10.3788/OPE.20172511.2939

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基于多波段深度神经网络的舰船目标识别

信息科学 | 更新时间：2020-07-07

- 基于多波段深度神经网络的舰船目标识别
- Ship recognition based on multi-band deep neural network
- 光学精密工程 2017年25卷第11期页码：2939-2946
- 作者机构：
  
  1.海军航空工程学院控制工程系, 山东烟台 264001
  2.中国国防科技信息中心, 北京 100142
  3.91206部队, 山东青岛 266108
- 作者简介：
  
  [ "刘峰(1988-), 男, 黑龙江哈尔滨人, 博士研究生, 2011年于华中科技大学获得学士学位, 2013年于海军航空工程学院获得硕士学位, 主要从事机器视觉及目标检测识别等研究。E-mail:liufeng_cv@126.com" ]
  [ "马新星(1982-), 男, 江苏如牟人, 博士研究生, 2005年, 2013年于海军航空工程学院分别获得学士学位、硕士学位, 主要从事红外图像处理、场景仿真、目标检测等研究。E-mail:xinxing_ma@tom.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61303192)
- DOI：10.3788/OPE.20172511.2939
  中图分类号： TP391.4
- 收稿日期：2017-04-11，
  
  录用日期：2017-6-30，
  
  纸质出版日期：2017-11-25
- 稿件说明：
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刘峰, 沈同圣, 马新星, 等. 基于多波段深度神经网络的舰船目标识别[J]. 光学精密工程, 2017,25(11):2939-2946.

Feng LIU, Tong-sheng SHEN, Xin-xing MA, et al. Ship recognition based on multi-band deep neural network[J]. Optics and precision engineering, 2017, 25(11): 2939-2946.
刘峰, 沈同圣, 马新星, 等. 基于多波段深度神经网络的舰船目标识别[J]. 光学精密工程, 2017,25(11):2939-2946. DOI： 10.3788/OPE.20172511.2939.

Feng LIU, Tong-sheng SHEN, Xin-xing MA, et al. Ship recognition based on multi-band deep neural network[J]. Optics and precision engineering, 2017, 25(11): 2939-2946. DOI： 10.3788/OPE.20172511.2939.

摘要

考虑多波段图像的融合识别可以扩展识别系统的应用范围，本文探索并设计了一种基于卷积神经网络的融合识别方法。该方法以AlexNet网络模型为基础，同时对可见光、中波红外和长波红外三波段图像进行特征提取；然后，利用互信息的方法对串联的三波段特征向量进行特征选择，依据重要性排序的方式选定固定长度的特征向量；最后，依据特征提取层级的不同，分别以早期融合、中期融合和后期融合3种融合方式来验证算法的有效性。采用自建的三波段舰船图像数据库进行了模型的训练和测试，共包含6类目标，5 000余张图像。实验结果显示，采用的3种融合识别方法中，中间层融合的识别准确率最高，达到84.5%，比早期融合和后期融合分别高5%和7%左右。另外，在本文的应用场景下，无论何种融合方式，其融合识别的准确率均明显高于其他单波段识别的准确率。

Abstract

The fusion recognition of multi-band images can extend the application range of recognition systems. A fusion method based on convolutional neural networks (CNN) was explored and designed in this paper. Based on the AlexNet network model

it was extracted that the ship target features of three wave band images concurrently in visible light

Middle Wave Infrared (MWIR) and Long Wave Infrared (LWIR) bands. Then

it performed the feature selection for concatenated three-band eigenvectors by using the mutual information method and determines the dimensions of fusion eigenvectors according to sorting the importance of concatenated feature eigenvectors. Finally

three fusion methods named as Early fusion

Middle fusion and Late fusion were used to verify respectively the effectiveness of the proposed algorithm according to the features extracted from different levels. An available ship target dataset in three bands containing 6 categories of targets and more than 5 000 images was established for our experimental verification. The results show that the recognition rate from Middle fusion reaches 84.5%. Compared with Early Fusion and Late Fusion

it increases by 8% and 12%. Moreover

the recognition rates of all three fusion methods have been improve significantly as compared to that of the single band recognitions at the same application scene.

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Keywords

references

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