基于属性散射中心卷积核调制的SAR目标识别深层网络

李毅 杜兰 周可儿 杜宇昂

李毅, 杜兰, 周可儿, 等. 基于属性散射中心卷积核调制的SAR目标识别深层网络[J]. 雷达学报(中英文), 2024, 13(2): 443–456. doi: 10.12000/JR24001
引用本文: 李毅, 杜兰, 周可儿, 等. 基于属性散射中心卷积核调制的SAR目标识别深层网络[J]. 雷达学报(中英文), 2024, 13(2): 443–456. doi: 10.12000/JR24001
LI Yi, DU Lan, ZHOU Ke’er, et al. Deep network for SAR target recognition based on attribute scattering center convolutional kernel modulation[J]. Journal of Radars, 2024, 13(2): 443–456. doi: 10.12000/JR24001
Citation: LI Yi, DU Lan, ZHOU Ke’er, et al. Deep network for SAR target recognition based on attribute scattering center convolutional kernel modulation[J]. Journal of Radars, 2024, 13(2): 443–456. doi: 10.12000/JR24001

基于属性散射中心卷积核调制的SAR目标识别深层网络

doi: 10.12000/JR24001
基金项目: 国家自然科学基金(U21B2039)
详细信息
    作者简介:

    李 毅,博士生,主要研究方向为SAR图像解译、机器学习与人工智能等

    杜 兰,博士,教授,主要研究方向为雷达目标识别、雷达信号处理、机器学习等

    周可儿,硕士生,主要研究方向为雷达智能目标识别

    杜宇昂,博士生,主要研究方向为SAR图像解译、机器学习与人工智能等

    通讯作者:

    杜兰 dulan@mail.xidian.edu.cn

  • 责任主编:朱卫纲 Corresponding Editor: ZHU Weigang
  • 中图分类号: TN957.51

Deep Network for SAR Target Recognition Based on Attribute Scattering Center Convolutional Kernel Modulation

Funds: The National Natural Science Foundation of China (U21B2039)
More Information
  • 摘要: 卷积神经网络(CNN)的特征提取能力与其参数量有关,一般来说,参数量越多,CNN的特征提取能力越强。但要学好这些参数需要大量的训练数据,而在实际应用中,可用于模型训练的合成孔径雷达(SAR)图像往往是有限的。减少CNN的参数量可以降低对训练样本的需求,但同时也会降低CNN的特征表达能力,影响其目标识别性能。针对此问题,该文提出一种基于属性散射中心(ASC)卷积核调制的SAR目标识别深层网络。由于SAR图像具有电磁散射特性,为了提取更符合SAR目标特性的散射结构和边缘特征,所提网络使用预先设定的具有不同指向和长度的ASC核对少量CNN卷积核进行调制以生成更多卷积核,从而在降低网络参数量的同时保证其特征提取能力。此外,该网络在浅层使用ASC调制卷积核来提取更符合SAR图像特性的散射结构和边缘特征,而在高层使用CNN卷积核来提取SAR图像的语义特征。由于同时使用ASC调制卷积核和CNN卷积核,该网络能够兼顾SAR目标的电磁散射特性和CNN的特征提取优势。使用实测SAR图像进行的实验证明了所提网络可以在降低对训练样本需求的同时保证优秀的SAR目标识别性能。

     

  • 图  1  基于ASC卷积核调制的SAR目标识别深层网络的结构图

    Figure  1.  Architecture of the deep network for SAR target recognition based on ASC convolutional kernel modulation

    图  2  根据预设的L$\bar \varphi $的组合得到的一组$5 \times 5$的ASC核(红框内的ASC核为最终选定的ASC核)

    Figure  2.  A set of ASC kernels obtained according to the combinations of L and $\bar \varphi $ (the ASC kernels within the red box are the final selected ASC kernels)

    图  3  ASC调制卷积核的生成过程

    Figure  3.  The generation process of ASC modulated convolutional kernel

    图  4  ASC调制卷积核的卷积过程

    Figure  4.  The convolution process of ASC modulated convolutional kernel

    图  5  使用ASC调制卷积核的卷积层中的完整卷积过程示例

    Figure  5.  An example of the complete convolution process in a convolutional layer using ASC modulated convolution kernels

    图  6  MSTAR 10类不同地面目标的SAR图像及其对应的光学图像示例

    Figure  6.  Examples of SAR images and corresponding optical images of ten different types of ground targets in MSTAR dataset

    图  7  OpenSARShip舰船目标SAR图像示例

    Figure  7.  Examples of SAR images in OpenSARShip dataset

    表  1  MSTAR的3类7型子数据集的具体信息

    Table  1.   The detailed information of three-target MSTAR data

    类别 型号 训练样本数 测试样本数
    BMP2 SNC21 233 196
    SN9563 0 195
    SN9566 0 196
    BTR70 C71 233 196
    T72 SNS7 0 191
    SN132 232 196
    SN812 0 195
    下载: 导出CSV

    表  2  MSTAR的10类14型子数据集的具体信息

    Table  2.   The detailed information of ten-target MSTAR data

    类别训练样本数测试样本数
    BTR60255195
    2S1299274
    BRDM2298274
    D7299274
    T62299273
    ZIL131299274
    ZSU23/4299274
    BMP2233587
    BTR70233196
    T72232582
    下载: 导出CSV

    表  3  OpenSARShip的3类数据集具体信息

    Table  3.   The detailed information of three-target OpenSARShip data

    类别训练样本数测试样本数
    Cargo241159
    Fishing9223
    Tanker11576
    下载: 导出CSV

    表  4  不同方法对3类7型MSTAR子数据集的识别性能以及各方法参数量

    Table  4.   Recognition performance of different methods on three-target MSTAR data and the number of parameters of each method

    方法参数量PCC
    VGG16$6.51 \times {10^7}$0.9319
    ResNet34$2.13 \times {10^7}$0.9253
    A-ConvNet$3.03 \times {10^5}$0.9385
    BaseNet$2.27 \times {10^6}$0.9495
    CA-MCNN$6.52 \times {10^6}$0.9861
    所提方法$2.26 \times {10^6}$0.9875
    下载: 导出CSV

    表  5  不同方法对10类14型MSTAR子数据集的识别性能以及各方法参数量

    Table  5.   Recognition performance of different methods on ten-target MSTAR data and the number of parameters of each method

    方法参数量PCC
    VGG16$6.51 \times {10^7}$0.9166
    ResNet34$2.13 \times {10^7}$0.9138
    A-ConvNet$3.03 \times {10^5}$0.9219
    BaseNet$2.27 \times {10^6}$0.9422
    CA-MCNN$6.52 \times {10^6}$0.9781
    所提方法$2.26 \times {10^6}$0.9844
    下载: 导出CSV

    表  6  不同方法对OpenSARShip的3类数据集的识别性能以及各方法参数量

    Table  6.   Recognition performance of different methods on three-target OpenSARShip data and the number of parameters of each method

    方法 参数量 PCC
    VGG16 $6.51 \times {10^7}$ 0.7713
    ResNet34 $2.13 \times {10^7}$ 0.7403
    A-ConvNet $3.03 \times {10^5}$ 0.7791
    BaseNet $2.27 \times {10^6}$ 0.8062
    CA-MCNN $6.52 \times {10^6}$
    所提方法 $2.26 \times {10^6}$ 0.8101
    下载: 导出CSV

    表  7  预设的ASC核的方向数取不同的M值时对3类7型MSTAR子数据集的识别性能

    Table  7.   Recognition performance on three-target MSTAR data under different values of M

    方向数M PCC
    2 0.9722
    3 0.9817
    4 0.9875
    5 0.9810
    6 0.9744
    下载: 导出CSV

    表  8  预设的ASC核的长度L取不同值时对3类7型MSTAR子数据集的识别性能

    Table  8.   Recognition performance on three-target MSTAR data under different values of L

    长度L PCC
    0.3 0.9729
    0.6 0.9788
    0.9 0.9875
    1.2 0.9810
    下载: 导出CSV

    表  9  不同层卷积层采用ASC调制卷积核时对3类7型MSTAR子数据集的识别性能

    Table  9.   Recognition performance on three-target MSTAR data under different convolution layers using ASC modulated convolutional kernels

    采用层 VGG16 ResNet18 所提方法
    不采用 0.9319 0.9480 0.9495
    第1层 0.9795 0.9663 0.9795
    第1, 2层 0.9839 0.9769 0.9875
    第1~3层 0.9773 0.9736 0.9810
    第1~4层 0.9758 0.9729 0.9736
    第2, 3层 0.9766 0.9641 0.9751
    第3, 4层 0.9714 0.9582 0.9707
    下载: 导出CSV

    表  10  不同方法在不同训练样本数下对3类7型MSTAR子数据集的识别性能

    Table  10.   Recognition performance of different methods on three-target MSTAR data with different number of training samples

    方法 训练样本比例
    100% 50% 30% 25% 20%
    VGG16 0.9319 0.8886 0.8352 0.7883 0.7370
    ResNet34 0.9253 0.8813 0.8278 0.7590 0.7267
    A-ConvNet 0.9385 0.9062 0.8769 0.8337 0.7875
    BaseNet 0.9495 0.8974 0.8674 0.8227 0.7758
    CA-MCNN 0.9861 0.9641 0.9165 0.8938 0.8608
    所提方法 0.9875 0.9670 0.9480 0.9187 0.8711
    下载: 导出CSV
  • [1] KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. ImageNet classification with deep convolutional neural networks[C]. The 25th International Conference on Neural Information Processing Systems, Lake Tahoe, USA, 2012: 1006–1114.
    [2] SIMONYAN K and ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]. 3rd International Conference on Learning Representations, San Diego, USA, 2015: 1–14. doi: 10.48550/arXiv.1409.1556.
    [3] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778. doi: 10.1109/CVPR.2016.90.
    [4] TAN Mingxing and LE Q. EfficientNet: Rethinking model scaling for convolutional neural networks[C]. The 36th International Conference on Machine Learning, Long Beach, USA, 2019: 6105–6114.
    [5] LIU Ze, LIN Yutong, CAO Yue, et al. Swin transformer: Hierarchical vision transformer using shifted windows[C]. 2021 IEEE/CVF International Conference on Computer Vision, Montreal, Canada, 2021: 9992–10002. doi: 10.1109/ICCV48922.2021.00986.
    [6] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[C]. 9th International Conference on Learning Representations, 2021: 1−22. https://iclr.cc/virtual/2021/index.html.
    [7] CHEN Sizhe, WANG Haipeng, XU Feng, et al. Target classification using the deep convolutional networks for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4806–4817. doi: 10.1109/TGRS.2016.2551720.
    [8] 喻玲娟, 王亚东, 谢晓春, 等. 基于FCNN和ICAE的SAR图像目标识别方法[J]. 雷达学报, 2018, 7(5): 622–631. doi: 10.12000/JR18066.

    YU Lingjuan, WANG Yadong, XIE Xiaochun, et al. SAR ATR based on FCNN and ICAE[J]. Journal of Radars, 2018, 7(5): 622–631. doi: 10.12000/JR18066.
    [9] 赵鹏菲, 黄丽佳. 一种基于EfficientNet与BiGRU的多角度SAR图像目标识别方法[J]. 雷达学报, 2021, 10(6): 895–904. doi: 10.12000/JR20133.

    ZHAO Pengfei and HUANG Lijia. Target recognition method for multi-aspect synthetic aperture radar images based on EfficientNet and BiGRU[J]. Journal of Radars, 2021, 10(6): 895–904. doi: 10.12000/JR20133.
    [10] HUANG Xiayuan, YANG Qiao, and QIAO Hong. Lightweight two-stream convolutional neural network for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(4): 667–671. doi: 10.1109/LGRS.2020.2983718.
    [11] LIU Jiaming, XING Mengdao, YU Hanwen, et al. EFTL: Complex convolutional networks with electromagnetic feature transfer learning for SAR target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5209811. doi: 10.1109/TGRS.2021.3083261.
    [12] ZHANG Tianwen, ZHANG Xiaoling, KE Xiao, et al. HOG-ShipCLSNet: A novel deep learning network with HOG feature fusion for SAR ship classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5210322. doi: 10.1109/TGRS.2021.3082759.
    [13] QOSJA D, WAGNER S, and BRÜGGENWIRTH S. Benchmarking convolutional neural network backbones for target classification in SAR[C]. 2023 IEEE Radar Conference, San Antonio, USA, 2023: 1–6. doi: 10.1109/RadarConf2351548.2023.10149802.
    [14] LIU Zhuang, MAO Hanzi, WU Chaoyuan, et al. A ConvNet for the 2020s[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 11966–11976. doi: 10.1109/CVPR52688.2022.01167.
    [15] 张翼鹏, 卢东东, 仇晓兰, 等. 基于散射点拓扑和双分支卷积神经网络的SAR图像小样本舰船分类[J]. 雷达学报, 2024, 13(2): 411–427. doi: 10.12000/JR23172.

    ZHANG Yipeng, LU Dongdong, QIU Xiaolan, et al. Few-shot ship classification of SAR images via scattering point topology and dual-branch convolutional neural network[J]. Journal of Radars, 2024, 13(2): 411–427. doi: 10.12000/JR23172.
    [16] LUAN Shangzhen, CHEN Chen, ZHANG Baochang, et al. Gabor convolutional networks[J]. IEEE Transactions on Image Processing, 2018, 27(9): 4357–4366. doi: 10.1109/TIP.2018.2835143.
    [17] 徐丰, 金亚秋. 微波视觉与SAR图像智能解译[J]. 雷达学报, 2024, 13(2): 285–306. doi: 10.12000/JR23225.

    XU Feng and JIN Yaqiu. Microwave vision and intelligent perception of radar imagery[J]. Journal of Radars, 2024, 13(2): 285–306. doi: 10.12000/JR23225.
    [18] GERRY M J, POTTER L C, GUPTA I J, et al. A parametric model for synthetic aperture radar measurements[J]. IEEE Transactions on Antennas and Propagation, 1999, 47(7): 1179–1188. doi: 10.1109/8.785750.
    [19] POTTER L C and MOSES R L. Attributed scattering centers for SAR ATR[J]. IEEE Transactions on Image Processing, 1997, 6(1): 79–91. doi: 10.1109/83.552098.
    [20] 李飞. 雷达图像目标特征提取方法研究[D]. [博士论文], 西安电子科技大学, 2014.

    LI Fei. Study on target feature extraction based on radar image[D]. [Ph.D. dissertation], Xidian University, 2014.
    [21] ROSS T D, WORRELL S W, VELTEN V J, et al. Standard SAR ATR evaluation experiments using the MSTAR public release data set[C]. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery V, Orlando, USA, 1998: 566–573. doi: 10.1117/12.321859.
    [22] HUANG Lanqing, LIU Bin, LI Boying, et al. OpenSARShip: A dataset dedicated to sentinel-1 ship interpretation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(1): 195–208. doi: 10.1109/JSTARS.2017.2755672.
    [23] SUN Yongguang, DU Lan, WANG Yan, et al. SAR automatic target recognition based on dictionary learning and joint dynamic sparse representation[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1777–1781. doi: 10.1109/LGRS.2016.2608578.
    [24] DENG Sheng, DU Lan, LI Chen, et al. SAR automatic target recognition based on Euclidean distance restricted autoencoder[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(7): 3323–3333. doi: 10.1109/JSTARS.2017.2670083.
    [25] NI Jiacheng and XU Yuelei. SAR automatic target recognition based on a visual cortical system[C]. 2013 6th International Congress on Image and Signal Processing, Hangzhou, China, 2013: 778–782. doi: 10.1109/CISP.2013.6745270.
    [26] LI Yi, DU Lan, and WEI Di. Multiscale CNN based on component analysis for SAR ATR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5211212. doi: 10.1109/TGRS.2021.3100137.
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出版历程
  • 收稿日期:  2024-01-04
  • 修回日期:  2024-03-13
  • 网络出版日期:  2024-03-27
  • 刊出日期:  2024-04-28

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