深度学习在SAR目标识别与地物分类中的应用

徐丰 王海鹏 金亚秋

徐丰, 王海鹏, 金亚秋. 深度学习在SAR目标识别与地物分类中的应用[J]. 雷达学报, 2017, 6(2): 136-148. doi: 10.12000/JR16130
引用本文: 徐丰, 王海鹏, 金亚秋. 深度学习在SAR目标识别与地物分类中的应用[J]. 雷达学报, 2017, 6(2): 136-148. doi: 10.12000/JR16130
Xu Feng, Wang Haipeng, Jin Yaqiu. Deep Learning as Applied in SAR Target Recognition and Terrain Classification[J]. Journal of Radars, 2017, 6(2): 136-148. doi: 10.12000/JR16130
Citation: Xu Feng, Wang Haipeng, Jin Yaqiu. Deep Learning as Applied in SAR Target Recognition and Terrain Classification[J]. Journal of Radars, 2017, 6(2): 136-148. doi: 10.12000/JR16130

深度学习在SAR目标识别与地物分类中的应用

doi: 10.12000/JR16130
基金项目: 国家自然科学基金(61571132, 61571134, 61331020),上海航天科技创新基金
详细信息
    作者简介:

    徐 丰(1982–),男,浙江东阳人,复旦大学博士学位,教授,复旦大学电磁波信息科学教育部重点实验室副主任,研究方向为SAR图像解译、电磁散射建模、人工智能,兼职: IEEE地球科学与遥感快报副主编、 IEEE地球科学与遥感学会上海分会主席。E-mail: fengxu@fudan.edu.cn

    王海鹏(1979–),男,河南遂平人,复旦大学电磁波信息科学教育部重点实验室副教授,研究方向为雷达系统设计与算法开发、遥感图像处理与信息获取、机器学习与目标识别、智能图像处理等。E-mail: hpwang@fudan.edu.cn

    金亚秋(1946–),男,上海人,美国麻省理工学院博士学位,教授,复旦大学电磁波信息科学教育部重点实验室主任,中国科学院院士,研究方向为复杂自然介质的电磁辐射、散射与传输。E-mail: yqjin@fudan.edu.cn

    通讯作者:

    徐丰   fengxu@fudan.edu.cn

  • 中图分类号: TN959

Deep Learning as Applied in SAR Target Recognition and Terrain Classification

Funds: The National Natural Science Foundation of China (61571132, 61571134, 61331020), The Foundation of Shanghai Aerospace Science and Technology
  • 摘要:

    深度卷积网络等深度学习算法变革了计算机视觉领域,在多种应用上的效果都超过了以往传统图像处理算法。该文简要回顾了将深度学习应用在SAR图像目标识别与地物分类中的工作。利用深度卷积网络从SAR图像中自动学习多层的特征表征,再利用学习到的特征进行目标检测与目标分类。将深度卷积网络应用于SAR目标分类数据集MSTAR上,10类目标平均分类精度达到了99%。针对带相位的极化SAR图像,该文提出了复数深度卷积网络,将该算法应用于全极化SAR图像地物分类,Flevoland 15类地物平均分类精度达到了95%。

     

  • 图  1  深度卷积网络示意图[9]

    Figure  1.  Deep convolutional neural networks[9]

    图  2  层次化可组合的特性表征能力

    Figure  2.  Hierarchical compositional feature representation capability

    图  3  两种激活函数及其梯度比较

    Figure  3.  Two types of activation function and their gradients

    图  4  通过ReLU的非线性解决XOR问题

    Figure  4.  XOR problem solved by using the nonlinearity of ReLU

    图  5  复数卷积神经网络结构[10]

    Figure  5.  Structure of complex-valued convolutional neural network[10]

    图  6  网络整体结构,包含5个可训练层。卷积层表示为“conv.(卷积核数量)@(卷积核大小)”

    Figure  6.  Network architecture, including 5 trainable layers. Convolution layer is denoted as “conv.(kernel depth)@(kernel size)”

    图  7  10类军事目标示例:光学图像 vs. SAR图像

    Figure  7.  Ten-class military targets: optical image vs. SAR image

    图  8  实数卷积神经网络结构

    Figure  8.  Real-valued convolutional neural network architecture

    图  9  全极化L-波段旧金山地区AirSAR图像

    Figure  9.  Fully-polarimetric L-band AirSAR image of San Francisco area

    图  10  CV-CNN的整体结构

    Figure  10.  Architecture of CV-CNN

    图  11  Flevoland数据

    Figure  11.  Flevoland data

    图  12  分类结果

    Figure  12.  Classification result

    图  13  用于训练的南京某地区ALOS2图像

    Figure  13.  ALOS2 image of Nanjing used as training data

    图  14  用于测试的南京另一区域的结果

    Figure  14.  Results of another region in Nanjing used for testing

    图  15  用于测试的上海某区域的ALOS图像及其分类结果和对比光学影像

    Figure  15.  Results of Shanghai area used for testing

    图  16  数据驱动-模型约束下的SAR智能解译

    Figure  16.  Data driven-model regularized intelligent SAR imagery interpretation

    表  1  SAR图像解译与计算机视觉的差异

    Table  1.   Comparison of SAR imagery interpretation and computer vision

    特性 SAR图像解译 计算机视觉
    波段 微波 可见光
    成像原理 相位相干叠加 能量聚焦叠加
    投影方向 距离向-方位角 俯仰角-方位角
    典型视角 空天对地观测 第一人称视角
    数据构成 幅度相位、多通道、多模式 RGB、视频
    下载: 导出CSV

    表  2  SOC实验条件下的混淆矩阵

    Table  2.   Confusion matrix under SOC setting

    Class BMP-2 BTR-70 T-72 BTR-60 2S1 BRDM-2 D7 T-62 ZIL-131 ZSU-234 Pcc (%)
    BMP-2 194 0 1 0 1 0 0 0 0 0 98.98
    BTR-70 0 195 0 0 0 1 0 0 0 0 99.49
    T-72 0 0 196 0 0 0 0 0 0 0 100
    BTR-60 1 0 0 188 0 0 0 1 1 4 96.41
    2S1 0 0 0 0 269 4 0 0 0 1 98.18
    BRDM-2 0 0 0 0 0 272 0 0 0 2 99.27
    D7 0 0 0 0 0 0 272 1 1 0 99.27
    T-62 0 0 0 0 0 0 0 272 1 0 99.64
    ZIL-131 0 0 0 0 0 0 0 0 273 1 99.64
    ZSU-234 0 0 0 0 0 0 1 0 0 273 99.64
    Total 99.13
    下载: 导出CSV

    表  3  Flevoland数据分类结果

    Table  3.   Flevoland result

    Class 训练样本数 测试样本数 实数网络准确率 复数网络正确率
    Stem beans 1245 282 100 100
    Peas 1225 308 99.26 98.38
    Forest 1184 295 99.36 99.66
    Lucerne 1225 293 93.16 99.66
    Wheat 1163 291 97.60 95.88
    Beet 1213 287 97.18 99.30
    Potatoes 1220 286 89.42 97.20
    Bare soil 1236 310 99.36 88.06
    Grasses 1202 329 71.43 91.79
    Rapeseed 1216 290 87.58 91.72
    Barley 1207 311 98.49 99.36
    Wheat2 1176 304 83.23 92.43
    Wheat3 1224 325 77.61 94.77
    Water 1138 284 59.27 96.13
    Buildings 126 24 100 100
    Total 17000 4219 89.49 95.97
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-11-29
  • 修回日期:  2017-03-14
  • 网络出版日期:  2017-04-28

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