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深度学习在高光谱图像分类领域的研究现状与展望

张号逵 李映 姜晔楠

张号逵, 李映, 姜晔楠. 深度学习在高光谱图像分类领域的研究现状与展望. 自动化学报, 2018, 44(6): 961-977. doi: 10.16383/j.aas.2018.c170190
引用本文: 张号逵, 李映, 姜晔楠. 深度学习在高光谱图像分类领域的研究现状与展望. 自动化学报, 2018, 44(6): 961-977. doi: 10.16383/j.aas.2018.c170190
ZHANG Hao-Kui, LI Ying, JIANG Ye-Nan. Deep Learning for Hyperspectral Imagery Classification: The State of the Art and Prospects. ACTA AUTOMATICA SINICA, 2018, 44(6): 961-977. doi: 10.16383/j.aas.2018.c170190
Citation: ZHANG Hao-Kui, LI Ying, JIANG Ye-Nan. Deep Learning for Hyperspectral Imagery Classification: The State of the Art and Prospects. ACTA AUTOMATICA SINICA, 2018, 44(6): 961-977. doi: 10.16383/j.aas.2018.c170190

深度学习在高光谱图像分类领域的研究现状与展望

doi: 10.16383/j.aas.2018.c170190
基金项目: 

陕西省国际科技合作计划项目 2017KW-006

西北工业大学博士论文创新基金 CX201816

国家重点研发计划项目 2016YFB0502502

预研领域基金课题 614023804016HK03002

详细信息
    作者简介:

    张号逵 西北工业大学计算机学院陕西省语音与图像信息处理重点实验室博士研究生.2016年于西北工业大学计算机学院陕西省语音与图像信息处理重点实验室获得计算机应用技术专业硕士学位.主要研究方向为图像处理, 深度学习, 高光谱图像分类.E-mail:hkzhang1991@mail.nwpu.edu.cn

    姜晔楠 西北工业大学计算机学院陕西省语音与图像信息处理重点实验室博士研究生.2017年于东北林业大学获得计算机科学与技术专业硕士学位.主要研究方向为高光谱图像分类, 深度学习.E-mail:ynjiang@mail.nwpu.edu.cn

    通讯作者:

    李映 西北工业大学计算机学院教授.2002年于西安电子科技大学雷达信号处理国家重点实验室电路与系统专业获博士学位.主要研究方向为图像处理, 计算智能, 信号处理.本文通信作者.E-mail:lybyp@nwpu.edu.cn

  • 本文责任编委 张军平

Deep Learning for Hyperspectral Imagery Classification: The State of the Art and Prospects

Funds: 

Shaanxi International Scientific and Technological Cooperation Project 2017KW-006

Sponsored by Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University CX201816

National Key Research and Development Program 2016YFB0502502

Foundation Project for Advanced Research Field 614023804016HK03002

More Information
    Author Bio:

    Ph. D. candidate at Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, the School of Computer Science, Northwestern Polytechnical University. He received his master degree in computer application technology from Shaanxi Provincial Key Laboratory of Speech and Image Information Processing in 2016. His research interest covers image processing, deep learning, and hyperspectral image classification

    Ph. D. candidate at Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, the School of Computer Science, Northwestern Polytechnical University. She received her master degree in computer science from Northeast Forestry University in 2017. Her research interest covers hyperspectral image processing and deep learning

    Corresponding author: LI Ying Professor at the School of Computer Science. She received her Ph. D. degree in electrical circuit and system, from the National Key Laboratory of Radar Signal Processing, Xidian University in 2002. Her research interest covers image processing, computation intelligence, and signal processing. Corresponding author of this paper
  • 摘要: 高光谱图像(Hyperspectral imagery,HSI)分类是高光谱遥感对地观测技术的一项重要内容,在军事及民用领域都有着重要的应用.然而,高光谱图像的高维特性、波段间高度相关性、光谱混合等使得高光谱图像分类面临巨大挑战.近年来,随着深度学习新技术的出现,基于深度学习的高光谱图像分类在方法和性能上得到了突破性的进展,为其研究提供了新的契机.本文首先介绍了高光谱图像分类的背景、研究现状及几个常用的数据集,并简要概述了几种典型的深度学习模型,最后详细介绍了当前的一些基于深度学习的高光谱图像分类方法,总结了深度学习在高光谱图像分类领域中的主要作用和存在的问题,并对未来的研究方向进行了展望.
    1)  本文责任编委 张军平
  • 图  1  高光谱图像示意图

    Fig.  1  Illustration of HSI

    图  2  CNN网络结构

    Fig.  2  The structure of CNN

    图  3  SAE网络结构

    Fig.  3  The structure of SAE

    图  4  DBN网络结构

    Fig.  4  The structure of DBN

    图  5  基于1D-CNN的高光谱图像谱特征分类

    Fig.  5  Spectral feature classification of HSI based on 1D-CNN

    图  6  基于2D-CNN的高光谱图像谱特征分类

    Fig.  6  Spatial feature classification of HSI based on 2D-CNN

    图  7  基于谱特征和空间特征分别提取的分类流程图

    Fig.  7  The flow chart of classification based on extracting spectral and spatial feature separately

    图  8  基于3D-CNN同时提取谱特征和空间特征的分类流程图

    Fig.  8  The flow chart of classification based on 3D-CNN extracting spectral and spatial feature simultaneously

    图  9  二维卷积和三维卷积

    Fig.  9  2D convolution operation and 3D convolution operation

    图  10  基于SAE的高光谱图像分类

    Fig.  10  HSI classification based on SAE

    图  11  去噪自编码示意图

    Fig.  11  The illustration of dAE

    图  12  基于DBN的高光谱图像分类

    Fig.  12  HSI classification based on DBN

    图  13  Indian Pines分类结果图

    Fig.  13  The classification of Indian Pines

    表  1  高光谱图像分类常用数据集

    Table  1  Several common datasets of HSI classification

    数据Indian PinesSalinasKennedy Space CenterPavia CenterPavia UniversityBotswana
    采集时间1992年1992年1996年2001年2001年2001年
    采集地点印第安纳州加利福尼亚州佛罗里达意大利北部意大利北部奥卡万戈三角洲
    采集设备AVIRISAVIRISAVIRISROSISROSISHyperion
    光谱覆盖范围($\mu$m)0.4 $\times$ 2.50.4 $\times$ 2.50.4 $\times$ 2.50.43 $\times$ 0.860.43 $\times$ 0.860.4 $\times$ 2.5
    数据大小(像素)145 $\times$ 145512 $\times$ 217512 $\times$ 6141 096 $\times$ 492610 $\times$ 3401 476 $\times$ 256
    空间分辨率(m)203.7181.31.330
    波段数224224224115115242
    去噪后波段数200204176102103145
    样本量10 24954 1295 2117 45642 7763 248
    类别数1616139914
    下载: 导出CSV

    表  2  几种主流的深度学习开发工具

    Table  2  Several mainstream development tools of deep learning

    工具机构支持语言官网
    TensorflowGooglePython/C++/Go/Javahttps://www.tensorflow.org/
    TheanoU MontrealPythonhttp://deeplearning.net/software/theano/
    PytorchFacebookPythonhttp://pytorch.org/
    CaffeBVLCC++/Python/Matlabhttp://caffe.berkeleyvision.org/
    CNTKMicrosoftC++/Python/Chttp://cntk.codeplex.com/
    Matconvnet/Matlabhttp://www.vlfeat.org/matconvnet/
    MXNetDMLCPython/C++/R/Julia/Scala/Go/Matlab/JavaScripthttp://mxnet.io/index.html
    TorchFacebookLuahttp://torch.ch/
    Deeplearning4JDeepLearning4JJava/Scalahttps://deeplearning4j.org/
    下载: 导出CSV

    表  3  Pavia University分类结果

    Table  3  The classification results of Pavia University

    模型谱特征空间特征空谱联合特征
    OA (%)AA (%)K ($\times$ 100)OA (%)AA (%)K ($\times$ 100)OA (%)AA (%)K ($\times$ 100)
    CNN[71]92.2892.5590.3794.0497.5292.4399.5499.7799.56
    SAE[31]95.1494.0193.7098.1297.3297.5598.5297.8298.07
    DBN[32]96.4295.0995.3098.6297.9598.1999.0598.4898.75
    下载: 导出CSV

    表  4  Indian Pines分类结果

    Table  4  The classification results of Indian Pines

    模型SAE[31]DBN[32]2D-CNN[64]3D-CNN[76]DC-CNN[71]
    OA (%)93.9895.9195.9799.0799.92
    AA (%)93.8194.2093.2398.6699.57
    K ($\times$ 100)93.1395.3495.4098.9399.91
    下载: 导出CSV
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  • 收稿日期:  2017-04-14
  • 录用日期:  2017-09-23
  • 刊出日期:  2018-06-20

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