计算机科学

计算机科学 ›› 2019, Vol. 46 ›› Issue (9): 265-270.doi: 10.11896/j.issn.1002-137X.2019.09.040

• 图形图像与模式识别 • 上一篇    下一篇

基于多路卷积神经网络的多光谱场景识别方法

江泽涛1,2, 秦嘉奇1, 胡硕3   

  1. (桂林电子科技大学广西图像图形智能处理高校重点实验室 广西 桂林541004)1;
    (桂林电子科技大学广西可信软件重点实验室 广西 桂林541004)2;
    (南昌航空大学 南昌330063)3
  • 收稿日期:2018-08-03 出版日期:2019-09-15 发布日期:2019-09-02
  • 通讯作者: 秦嘉奇(1993-),男,硕士生,主要研究方向为计算机视觉,E-mail:1445155606@qq.com
  • 作者简介:江泽涛(1961-),男,博士,教授,CCF会员,主要研究方向为计算机视觉;胡 硕(1983-),博士生,讲师,主要研究方向为智能计算。
  • 基金资助:
    国家自然科学基金(61572147),广西科技计划项目(AC16380108),广西图像图形智能处理重点实验室项目(GIIP201701),广西研究生教育创新计划资助项目(2018YJCX46),江西省自然科学基金资助项目(20171BAB212015)

Multi-spectral Scene Recognition Method Based on Multi-way Convolution Neural Network

JIANG Ze-tao1,2, QIN Jia-qi1, HU Shuo3   

  1. (The Key Laboratory of Image and Graphic Intelligent Processing of Higher Education in Guangxi,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China)1;
    (The Key Laboratory of Dependable Software of Guangxi,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China)2;
    (Nanchang Hangkong University,Nanchang 330063,China)3
  • Received:2018-08-03 Online:2019-09-15 Published:2019-09-02

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摘要： 现有的基于卷积神经网络的场景识别算法无法处理目标场景图形是多光谱图像的情况,在数据量较小的情况下,该算法的识别率不高。针对以上问题,提出一种基于多路卷积神经网络的多光谱场景识别方法。多路卷积神经网络接受三通道可见光彩色图像(RGB图像)以及单通道的近红外图像(NIR图像)共四通道输入。所提方法能够有效提取可见光图像特征、红外光图像特征以及可见光和红外光图像之间的关联特征,并将特征在全连接层进行融合,合理利用了各个光谱图像之间的相关信息,并通过结合预训练的方法来提高识别精度。在NIR_RGB数据集上的实验表明,与AlexNet、InceptionNet、ResNet以及人工设计特征描述子方法相比,该网络的平均识别率较高。并且,对此网络稍加改动,就能将其推广到其他多光谱图像分类任务中。

关键词: 场景识别, 多光谱, 卷积神经网络, 图像分类

Abstract: The existing scene recognition algorithm based on convolution neural network can’t deal with the multi spectral image of the target scene and can’t implement ideal accuracy in the case of insufficient data.In view of the above problems,this paper proposed a multi-spectral convolution neural network based multispectral scene recognition me-thod.The multi-way convolution neural network accepts three channels of visible light color image (RGB image) and a single channel near infrared image (NIR image) with a total of four channels.The proposed method can effectively extract the features of visible light image,infrared image and the correlation between visible and infrared images,and combine the features in the full connection layer,utilizing the correlation information among spectral images reasonably.The pre-training method is combined to improve the accuracy.Experiment results on the NIR_RGB dataset show that the average accuracy of the network is higher than that of AlexNet,InceptionNet,ResNet and artificial design feature descriptors.Moreover,this network can be extended to other multi-spectral image classification tasks with slight modification.

Key words: Convolution neural network, Image classification, Multi-spectral, Scene recognition

中图分类号: 

  • TP391.4
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