基于时频分析的SAR目标微波视觉特性智能感知方法与应用

黄钟泠 吴冲 姚西文 王立鹏 韩军伟

黄钟泠, 吴冲, 姚西文, 等. 基于时频分析的SAR目标微波视觉特性智能感知方法与应用[J]. 雷达学报(中英文), 2024, 13(2): 331–344. doi: 10.12000/JR23191
引用本文: 黄钟泠, 吴冲, 姚西文, 等. 基于时频分析的SAR目标微波视觉特性智能感知方法与应用[J]. 雷达学报(中英文), 2024, 13(2): 331–344. doi: 10.12000/JR23191
HUANG Zhongling, WU Chong, YAO Xiwen, et al. Physically explainable intelligent perception and application of SAR target characteristics based on time-frequency analysis[J]. Journal of Radars, 2024, 13(2): 331–344. doi: 10.12000/JR23191
Citation: HUANG Zhongling, WU Chong, YAO Xiwen, et al. Physically explainable intelligent perception and application of SAR target characteristics based on time-frequency analysis[J]. Journal of Radars, 2024, 13(2): 331–344. doi: 10.12000/JR23191

基于时频分析的SAR目标微波视觉特性智能感知方法与应用

doi: 10.12000/JR23191
基金项目: 国家自然科学基金(62101459),中国博士后科学基金(BX2021248)
详细信息
    作者简介:

    黄钟泠,副教授,硕士生导师,研究方向为SAR图像解译、深度学习和可解释人工智能等

    吴 冲,硕士生,研究方向为SAR目标识别等

    姚西文,副研究员,博士生导师,研究方向为计算机视觉、遥感图像处理、细粒度图像分类和目标识别等

    王立鹏,工程师,研究方向为智能感知总体设计、SAR图像智能检测识别等

    韩军伟,教授,博士生导师,研究方向为计算机视觉与脑成像分析等

    通讯作者:

    黄钟泠 huangzhongling@nwpu.edu.cn

    姚西文 yaoxiwen@nwpu.edu.cn

  • 责任主编:徐丰 Corresponding Editor: XU Feng
  • 中图分类号: TN957.51

Physically Explainable Intelligent Perception and Application of SAR Target Characteristics Based on Time-frequency Analysis

Funds: The National Natural Science Foundation of China (62101459), China Postdoctoral Science Foundation (BX2021248)
More Information
  • 摘要: 合成孔径雷达(SAR)目标识别智能算法目前仍面临缺少鲁棒性、泛化性和可解释性的挑战,理解SAR目标微波特性并将其结合先进的深度学习算法,实现高效鲁棒的SAR目标识别,是目前领域较为关注的研究重点。SAR目标特性反演方法通常计算复杂度较高,难以结合深度神经网络实现端到端的实时预测。为促进SAR目标物理特性在智能识别任务中的应用,发展高效、智能、可解释的微波物理特性感知方法至关重要。该文将高分辨SAR目标的非平稳特性作为一种典型的微波视觉特性,提出一种改进的基于时频分析的目标特性智能感知方法,优化了处理流程和计算效率,使之更适用于SAR目标识别场景,并进一步将其应用到SAR目标智能识别算法中,实现了稳定的性能提升。该方法泛化性强、计算效率高,能得到物理可解释的SAR目标特性分类结果,对目标识别算法的性能提升与属性散射中心模型相当。

     

  • 图  1  微波视觉特性智能感知与应用流程

    Figure  1.  Intelligent perception and application flow of microwave visual characteristics

    图  3  分割算法处理T-72目标的示例

    Figure  3.  Example of a segmentation algorithm dealing with a T-72 target

    图  2  分割算法流程

    Figure  2.  Segmentation algorithm flow

    图  4  参数$ {N_{{\text{rg}}}} $, $ {N_{{\text{az}}}} $, $ {N_{\text{f}}} $, $ {N_{{\text{win}}}} $等示意图

    Figure  4.  Schematic diagram of parameters $ {N_{{\text{rg}}}} $, $ {N_{{\text{az}}}} $, $ {N_{\text{f}}} $ and $ {N_{{\text{win}}}} $

    图  5  基于PIHA的网络的结构

    Figure  5.  Structure of a PIHA-based network

    图  6  $ {N_{{\mathrm{rg}}}} $= 64时,不同$ {N_{\mathrm{f}}} $设置下的汉明窗滤波器组

    Figure  6.  Hamming window filter groups at different settings of ${N_{\mathrm{f}}} $ when $ {N_{{\mathrm{rg}}}} $ = 64

    图  7  不同参数设置下的目标二维子带散射特性

    Figure  7.  Scattering characteristics of the target in 2D sub-bands with different parameter settings

    图  8  索贝尔算子处理结果(红色标记代表人造目标,黄色标记代表背景杂波)

    Figure  8.  The processing results of the Sobel operator (the red mark represents the artificial target, and the yellow mark represents the background clutter)

    图  9  ASC模型散射中心仿真结果的子带散射特性分析

    Figure  9.  Analysis of the scattering characteristics of the sub-band scattering characteristics of ASC model scattering center simulation results

    图  10  对T-72目标不同部件的子带散射特性分析(x轴和y轴分别为方位向和距离向)

    Figure  10.  Analysis of the sub-band scattering characteristics of different components of the T-72 target (the x-axis andy-axis are azimuth and distance, respectively)

    图  11  距离4种聚类中心最近的二维子带散射图

    Figure  11.  Scattering of the two-dimensional sub-bands closest to the four clustering centers

    图  12  T-72目标每种微波视觉特性的分布图

    Figure  12.  Distribution map of each microwave visual characteristic of T-72 target

    图  13  BMP目标每种微波视觉特性的分布图

    Figure  13.  Distribution map of each microwave visual characteristic of BMP target

    表  1  MSTAR数据集OFA评价方法设置

    Table  1.   OFA evaluation method settings for MSTAR dataset

    类别 训练集/验证集 OFA-1 OFA-2 OFA-3
    型号 俯仰角 数量 型号 俯仰角 数量 型号 俯仰角 数量 型号 俯仰角 数量
    BMP-2 9563 17° 233 9563 15° 195 9563 15° 195 / / /
    / / / / / / 9566 15° 196 / / /
    / / / / / / C21 15° 196 / / /
    T-72 132 17° 232 132 15° 196 132 15° 196 / / /
    / / / / / / 812 15° 195 / / /
    / / / / / / S7 15° 191 / / /
    BTR-70 C71 17° 233 C71 15° 196 C71 15° 196 / / /
    BTR-60 k10yt7532 17° 256 k10yt7532 15° 195 k10yt7532 15° 195 / / /
    2S1 b01 17° 299 b01 15° 274 b01 15° 274 b01 15°/30°/45° 274/288/303
    BRDM-2 E-71 17° 298 E-71 15° 274 E-71 15° 274 E-71 15°/30°/45° 274/420/423
    D7 92v13015 17° 299 92v13015 15° 274 92v13015 15° 274 / / /
    T-62 A51 17° 299 A51 15° 273 A51 15° 273 / / /
    ZIL-131 E12 17° 299 E12 15° 274 E12 15° 274 / / /
    ZSU-234 d08 17° 299 d08 15° 274 d08 15° 274 d08 15°/30°/45° 274/406/422
    下载: 导出CSV

    表  2  不同聚类中心数量对比试验(%)

    Table  2.   Comparative experiments on the number of centers in different clusters (%)

    聚类数量 90 50
    OFA-1 OFA-2 OFA-3 OFA-1 OFA-2 OFA-3
    2 96.49±1.61 93.99$\pm $0.81 62.32±2.11 94.54±0.98 90.57$\pm $0.98 61.96$\pm $0.92
    4 97.40±0.38 94.38±0.74 64.55±1.54 94.79±1.30 91.27±1.95 61.30$\pm $1.30
    8 96.95$\pm $0.86 93.44±0.90 63.26$\pm $3.34 94.58$\pm $1.39 90.53±2.08 59.14±4.45
    聚类数量 30 10
    OFA-1 OFA-2 OFA-3 OFA-1 OFA-2 OFA-3
    2 88.48±0.91 84.90$\pm $1.20 60.95±2.39 69.58±1.02 64.31$\pm $1.65 54.78±2.61
    4 89.10$\pm $1.36 85.05±1.49 60.86$\pm $1.03 70.23$\pm $1.94 63.92±2.13 54.24$\pm $1.29
    8 89.13±1.20 84.56±1.68 56.42±2.66 70.85±1.82 64.85±1.46 53.41±2.43
    注:加粗和下划线分别表示最优和次优结果。
    下载: 导出CSV

    表  3  基于ASC和微波视觉特性的PIHA对比实验(%)

    Table  3.   Comparative experiments of PIHA based on ASC and microwave visual characteristics (%)

    模型 90 50
    OFA-1 OFA-2 OFA-3 OFA-1 OFA-2 OFA-3
    DenseNet-121 95.37±1.04 91.73±0.76 60.37±2.66 91.60±1.82 88.45±1.58 60.18±1.57
    +PIHA (ASC) 97.41±0.99 94.40±1.66 62.07±2.40 95.32±1.30 91.97±1.72 61.81±1.16
    +PIHA (TFA) 97.40±0.38 94.38±0.74 64.55±1.54 94.79±1.30 91.27±1.95 61.30±1.30
    A-ConvNet 86.95±5.69 84.51±6.06 57.16±3.07 92.48±5.00 88.88±4.82 62.80±2.31
    +PIHA (ASC) 94.96±2.84 93.02±3.24 57.91±1.80 93.13±3.24 89.06±3.54 59.11±2.89
    +PIHA (TFA) 88.78±4.12 86.46±4.24 61.37±3.69 92.81±4.43 89.45±3.87 60.15±1.16
    模型 30 10
    OFA-1 OFA-2 OFA-3 OFA-1 OFA-2 OFA-3
    DenseNet-121 84.72±1.13 80.29±1.21 57.44±2.56 65.58±3.49 59.32±3.21 56.63±3.18
    +PIHA (ASC) 90.20±1.43 85.98±1.73 61.91±1.45 71.97±1.97 65.89±2.08 53.87±1.85
    +PIHA (TFA) 89.10±1.36 85.05±1.49 60.86±1.03 70.23±1.94 63.92±2.13 54.24±1.29
    A-ConvNet 87.65±2.39 83.04±3.32 58.63±1.73 72.02±1.70 64.76±2.27 52.71±2.28
    +PIHA (ASC) 91.18±2.19 86.49±2.49 58.78±1.03 76.11±1.93 69.60±2.95 53.22±1.56
    +PIHA (TFA) 90.39±2.58 85.98±2.83 59.88±2.13 77.80±4.85 72.34±4.35 52.35±1.91
    注:加粗表示最优结果,括号内表示使用的物理信息。
    下载: 导出CSV

    表  4  本文方法与已有目标识别算法的对比(%)

    Table  4.   Comparative experiments between the proposed method and existing algorithms (%)

    方法 模型描述 输入 90 50
    OFA-1 OFA-2 OFA-3 OFA-1 OFA-2 OFA-3
    A-ConvNet 数据驱动 幅度 86.95±5.69 84.51±6.06 57.16±3.07 92.48±5.00 88.88±4.82 62.80±2.31
    FEC 数据+物理模型 复数 92.32±4.41 86.22±5.03 58.76±5.18 86.23±5.62 81.34±5.83 55.03±2.35
    MS-CVNets 数据驱动 复数 96.77$\pm $1.74 94.34$\pm $1.22 66.83±3.96 93.43$\pm $1.89 90.78$\pm $1.83 62.56$\pm $3.71
    本文方法 数据+物理模型 复数 97.40±0.38 94.38±0.74 64.55$\pm $0.15 94.79±1.30 91.27±1.95 61.30±1.30
    方法 模型描述 30 10
    OFA-1 OFA-2 OFA-3 OFA-1 OFA-2 OFA-3
    A-ConvNet 数据驱动 幅度 87.65$\pm $2.39 83.04$\pm $3.32 58.63$\pm $1.73 72.02±1.70 64.76±2.27 52.71$\pm $2.28
    FEC 数据+物理模型 复数 68.43±7.72 64.48±6.16 51.12±1.81 57.84±3.47 54.04±3.91 43.44±5.97
    MS-CVNets 数据驱动 复数 81.33±0.95 77.50±0.97 56.02±1.11 49.11±4.27 44.63±3.74 38.96±5.78
    本文方法 数据+物理模型 复数 89.10±1.36 85.05±1.49 60.86±1.03 70.23$\pm $1.94 63.92$\pm $2.13 54.24±1.29
    注:加粗和下划线分别表示最优和次优结果。
    下载: 导出CSV
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  • 收稿日期:  2023-10-06
  • 修回日期:  2023-11-28
  • 网络出版日期:  2023-12-29
  • 刊出日期:  2024-04-28

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