融合可微分渲染的SAR多视角样本增广

贾赫成 蒲欣洋 王燕妮 符士磊 徐丰

贾赫成, 蒲欣洋, 王燕妮, 等. 融合可微分渲染的SAR多视角样本增广[J]. 雷达学报(中英文), 2024, 13(2): 457–470. doi: 10.12000/JR24011
引用本文: 贾赫成, 蒲欣洋, 王燕妮, 等. 融合可微分渲染的SAR多视角样本增广[J]. 雷达学报(中英文), 2024, 13(2): 457–470. doi: 10.12000/JR24011
JIA Hecheng, PU Xinyang, WANG Yanni, et al. Multi-view sample augumentation for SAR based on differentiable SAR renderer[J]. Journal of Radars, 2024, 13(2): 457–470. doi: 10.12000/JR24011
Citation: JIA Hecheng, PU Xinyang, WANG Yanni, et al. Multi-view sample augumentation for SAR based on differentiable SAR renderer[J]. Journal of Radars, 2024, 13(2): 457–470. doi: 10.12000/JR24011

融合可微分渲染的SAR多视角样本增广

doi: 10.12000/JR24011
基金项目: 国家自然科学基金(61991422)
详细信息
    作者简介:

    贾赫成,博士生,主要研究方向为SAR图像解译

    蒲欣洋,博士生,主要研究方向为遥感图像解译、视觉基础模型

    王燕妮,博士生,主要研究方向为深度强化学习在电磁领域中的应用

    符士磊,博士生,主要研究方向为SAR图像解译、SAR三维重建

    徐 丰,博士,教授,主要研究方向为SAR图像解译、电磁散射建模、智能信息技术

    通讯作者:

    徐丰 fengxu@fudan.edu.cn

  • 责任主编:仇晓兰 Corresponding Editor: QIU Xiaolan
  • 中图分类号: TN95

Multi-view Sample Augumentation for SAR Based onDifferentiable SAR Renderer

Funds: The National Natural Science Foundation of China (61991422)
More Information
  • 摘要: 合成孔径雷达(SAR)因其全天候、全天时的监测能力在民用和军事领域得到广泛应用。近年来,深度学习已被广泛应用于SAR图像自动解译。然而,由于卫星轨道和观测角度的限制,SAR目标样本面临视角覆盖率不全的问题,这为学习型SAR目标检测识别算法带来了挑战。该文提出一种融合可微分渲染的SAR多视角样本生成方法,结合逆向三维重建和正向渲染技术,通过卷积神经网络(CNN)从少量SAR视角图像中反演目标三维表征,然后利用可微分SAR渲染器(DSR)渲染出更多视角样本,实现样本在角度维的插值。另外,方法的训练过程使用DSR构建目标函数,无需三维真值监督。根据仿真数据的实验结果,该方法能够有效地增加多视角SAR目标图像,并提高小样本条件下典型SAR目标识别率。

     

  • 图  1  SAR目标视角覆盖不足的原因分析

    Figure  1.  Analysis of insufficient view coverage for SAR targets

    图  2  SAR多视角样本增广算法框架

    Figure  2.  Overall framework of multi-view sample augumentation for SAR

    图  3  目标和阴影区域预处理

    Figure  3.  Preprocessing of target and shadow regions

    图  4  目标重建网络结构

    Figure  4.  Structure of the target reconstruction network

    图  5  目标照射图和阴影图生成示意

    Figure  5.  Illustration of illumination map and shadow map generation

    图  6  目标重建结果可视化

    Figure  6.  Visualization of target reconstruction results

    图  7  SAR车辆目标生成结果可视化

    Figure  7.  Visualization of SAR vehicle target generation results

    图  8  目标区域和背景区域像素的Gamma分布拟合

    Figure  8.  Gamma distribution fitting of pixel values in target and background regions

    图  9  不同视角数量下的目标重建结果评估

    Figure  9.  Evaluation of target reconstruction results with different numbers of views

    图  10  T72实测样本的目标和阴影区域分割示意

    Figure  10.  Illustration of target and shadow area segmentation for T72 measured samples

    图  11  基于实测数据重建的目标三维模型可视化

    Figure  11.  Visualization of target 3D models reconstructed based on measured data

    图  12  生成图像中的目标和阴影区域

    Figure  12.  Target and shadow areas in generated images

    图  13  阴影区域形态学处理示意

    Figure  13.  Illustration of morphological processing in shadow areas

    图  14  不同阴影分割准确性下的实验结果

    Figure  14.  Experimental results under different shadow segmentation accuracies

    图  15  各类别中间结果的三维模型可视化

    Figure  15.  Visualization of intermediate 3D models for each category

    图  16  各类别生成样本与真值可视化对比

    Figure  16.  Visualization comparison of generated samples and ground truth for each category

    图  17  扩充前后识别结果混淆矩阵

    Figure  17.  Confusion matrices of recognition results before and after augmentation

    表  1  仿真数据集信息

    Table  1.   Information of the simulated dataset

    实验数据集 入射角$ \alpha $ 方位角$ \beta $
    训练集 {30°, 40°, 50°, 60°} {0°, 45°, 90°, ···, 315°}
    测试集1 {30°, 40°, 50°, 60°} {30°, 75°, 120°, ···, 345°}
    测试集2 {35°, 45°, 55°, 65°} {30°, 75°, 120°, ···, 345°}
    下载: 导出CSV

    表  2  各目标重建结果的mIoU

    Table  2.   mIoU of reconstruction results for each target

    目标类型训练集测试集1测试集2
    车辆0.84100.83750.8401
    飞机0.86290.85210.8554
    风车0.78600.78580.7880
    下载: 导出CSV

    表  3  小样本识别实验数据配置

    Table  3.   Configuration of experimental data for few-shot recognition

    训练数据 入射角$ \alpha $ 方位角$ \beta $ 样本数量
    训练集 {30°, 40°, 50°, 60°} {0°, 45°, 90°, ···, 315°} 320
    测试集 {5°, 15°, 25°, ···, 355°} 1440
    扩充1 {0°, 30°, 60°, ···, 330°} 480
    扩充2 {0°, 20°, 40°, ···, 340°} 720
    扩充3 {0°, 10°, 20°, ···, 350°} 1440
    下载: 导出CSV

    表  4  各类别重建三维模型结果评估

    Table  4.   Quantitative evaluation of intermediate 3D models for each category

    目标 mIoU $ {{L}}_{\mathrm{f}\mathrm{l}\mathrm{a}\mathrm{t}} $ 目标 mIoU $ {{L}}_{\mathrm{f}\mathrm{l}\mathrm{a}\mathrm{t}} $
    BRDM2 0.7919 0.0036 ZIL131 0.7995 0.0050
    BTR60 0.7812 0.0029 ZSU234 0.6000 0.0029
    D7 0.6653 0.0039 T72 0.6406 0.0032
    2S1 0.8052 0.0030 BMP2 0.7982 0.0022
    T62 0.7321 0.0040 BTR70 0.7781 0.0030
    下载: 导出CSV

    表  5  各类别散射纹理Gamma拟合参数估计

    Table  5.   Estimation of Gamma fitting parameters for scattering texture of each category

    目标 背景区域 目标区域
    参数a 参数b 参数a 参数b
    BRDM2 2.583 0.014 1.104 0.147
    BTR60 2.125 0.022 1.094 0.185
    D7 3.898 0.009 0.909 0.245
    2S1 3.597 0.012 1.145 0.169
    T62 3.331 0.010 1.070 0.191
    ZIL131 4.076 0.008 1.080 0.157
    ZSU234 3.144 0.009 0.955 0.216
    T72 2.550 0.018 1.066 0.202
    BMP2 2.561 0.018 1.027 0.191
    BTR70 2.610 0.018 1.179 0.141
    下载: 导出CSV

    表  6  样本扩充与小样本识别结果

    Table  6.   Results of few-shot recognition with sample augmentation

    训练数据 样本总数 旋转增强 准确率(%)
    VGG16 ResNet50 HRNet Swin-tiny
    原训练集 320 89.65 92.50 85.35 89.79
    95.69 96.87 94.86 95.83
    扩充1 800 94.24 96.94 95.76 94.24
    扩充2 1040 96.32 97.50 95.83 94.65
    扩充3 1760 95.90 97.29 95.69 96.39
    97.78 99.03 97.85 98.96
    下载: 导出CSV

    表  7  ResNet50的各类别识别结果准确率(%)

    Table  7.   Per-category recognition accuracy using ResNet50 (%)

    训练数据 BRDM2 BTR60 D7 2S1 T62 ZIL ZSU T72 BMP2 BTR70
    原始数据集 85.16 98.62 97.95 97.93 96.50 100 99.31 88.82 86.96 75.60
    扩充1 99.26 100 100 99.31 100 100 100 100 90.62 82.25
    扩充2 98.57 100 100 100 100 100 100 100 92.31 85.37
    扩充3 99.29 100 100 100 100 100 98.63 99.31 86.90 89.04
    下载: 导出CSV
  • [1] ZHANG Liangpei, ZHANG Lefei, and DU Bo. Deep learning for remote sensing data: A technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 2016, 4(2): 22–40. doi: 10.1109/MGRS.2016.2540798.
    [2] MA Lei, LIU Yu, ZHANG Xueliang, et al. Deep learning in remote sensing applications: A meta-analysis and review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 152: 166–177. doi: 10.1016/j.isprsjprs.2019.04.015.
    [3] ZHU Xiaoxiang, TUIA D, MOU Lichao, et al. Deep learning in remote sensing: A comprehensive review and list of resources[J]. IEEE Geoscience and Remote Sensing Magazine, 2017, 5(4): 8–36. doi: 10.1109/MGRS.2017.2762307.
    [4] SUN Xian, WANG Bing, WANG Zhirui, et al. Research progress on few-shot learning for remote sensing image interpretation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 2387–2402. doi: 10.1109/JSTARS.2021.3052869.
    [5] HUANG Zhongling, PAN Zongxu, and LEI Bin. What, where, and how to transfer in SAR target recognition based on deep CNNs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(4): 2324–2336. doi: 10.1109/TGRS.2019.2947634.
    [6] SHORTEN C and KHOSHGOFTAAR T M. A survey on image data augmentation for deep learning[J]. Journal of Big Data, 2019, 6(1): 60. doi: 10.1186/s40537-019-0197-0.
    [7] HENDRYCKS D, MU N, CUBUK E D, et al. AugMix: A simple data processing method to improve robustness and uncertainty[C]. 8th International Conference on Learning Representations, Addis Ababa, Ethiopia, 2010.
    [8] ALFASSY A, KARLINSKY L, AIDES A, et al. LaSO: Label-set operations networks for multi-label few-shot learning[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 6541–6550. doi: 10.1109/CVPR.2019.00671.
    [9] SCHWARTZ E, KARLINSKY L, SHTOK J, et al. Δ-encoder: An effective sample synthesis method for few-shot object recognition[C]. 32nd International Conference on Neural Information Processing Systems, Montréal, Canada, 2018: 2850–2860.
    [10] ANTONIOU A, STORKEY A, and EDWARDS H. Data augmentation generative adversarial networks[EB/OL]. https://arxiv.org/abs/1711.04340v3, 2018.
    [11] MALMGREN-HANSEN D, KUSK A, DALL J, et al. Improving SAR automatic target recognition models with transfer learning from simulated data[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(9): 1484–1488. doi: 10.1109/LGRS.2017.2717486.
    [12] GUO Jiayi, LEI Bin, DING Chibiao, et al. Synthetic aperture radar image synthesis by using generative adversarial nets[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(7): 1111–1115. doi: 10.1109/LGRS.2017.2699196.
    [13] SONG Qian, XU Feng, ZHU Xiaoxiang, et al. Learning to generate SAR images with adversarial autoencoder[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5210015. doi: 10.1109/TGRS.2021.3086817.
    [14] GUO Qian and XU Feng. Learning low-dimensional SAR target representations from few samples[C]. 2022 International Applied Computational Electromagnetics Society Symposium, Xuzhou, China, 2022: 1–2. doi: 10.1109/ACES-China56081.2022.10065101.
    [15] LIU Shichen, CHEN Weikai, LI Tianye, et al. Soft rasterizer: A differentiable renderer for image-based 3D reasoning[C]. 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), 2019: 7708–7717. doi: 10.1109/ICCV.2019.00780.
    [16] WANG Nanyang, ZHANG Yinda, LI Zhuwen, et al. Pixel2Mesh: Generating 3D mesh models from single RGB images[C]. 15th European Conference on Computer Vision, Munich, Germany, 2018: 52–67. doi: 10.1007/978-3-030-01252-6_4.
    [17] WEN Chao, ZHANG Yinda, LI Zhuwen, et al. Pixel2Mesh++: Multi-view 3D mesh generation via deformation[C]. 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), 2019: 1042–1051. doi: 10.1109/ICCV.2019.00113.
    [18] FU Shilei and XU Feng. Differentiable SAR renderer and image-based target reconstruction[J]. IEEE Transactions on Image Processing, 2022, 31: 6679–6693. doi: 10.1109/TIP.2022.3215069.
    [19] KATO H, USHIKU Y, and HARADA T. Neural 3D mesh renderer[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 3907–3916. doi: 10.1109/CVPR.2018.00411.
    [20] XU Feng and JIN Yaqiu. Imaging simulation of polarimetric SAR for a comprehensive terrain scene using the mapping and projection algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(11): 3219–3234. doi: 10.1109/TGRS.2006.879544.
    [21] FU Shilei, JIA Hecheng, PU Xinyang, et al. Extension of differentiable SAR renderer for ground target reconstruction from multiview images and shadows[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5217013. doi: 10.1109/TGRS.2023.3320515.
    [22] Moving and stationary target acquisition and recognition (MSTAR) public release data[EB/OL]. https://www.sdms.afrl.af.mil/index.php?collection=mstar.
    [23] 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.
    [24] 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.
    [25] SUN Ke, XIAO Bin, LIU Dong, et al. Deep high-resolution representation learning for human pose estimation[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 5693–5703. doi: 10.1109/CVPR.2019.00584.
    [26] 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: 10012–10022. DOi: 10.1109/ICCV48922.2021.00986.
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出版历程
  • 收稿日期:  2024-01-16
  • 修回日期:  2024-03-21
  • 网络出版日期:  2024-03-28
  • 刊出日期:  2024-04-28

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