基于改进GOFRO的多角度SAR图像车辆目标检测方法

刘琪 禹卫东 洪文

刘琪, 禹卫东, 洪文. 基于改进GOFRO的多角度SAR图像车辆目标检测方法[J]. 雷达学报, 2023, 12(5): 1081–1096. doi: 10.12000/JR23042
引用本文: 刘琪, 禹卫东, 洪文. 基于改进GOFRO的多角度SAR图像车辆目标检测方法[J]. 雷达学报, 2023, 12(5): 1081–1096. doi: 10.12000/JR23042
LIU Qi, YU Weidong, and HONG Wen. Vehicle detection in multi-aspect SAR images based on improved GOFRO[J]. Journal of Radars, 2023, 12(5): 1081–1096. doi: 10.12000/JR23042
Citation: LIU Qi, YU Weidong, and HONG Wen. Vehicle detection in multi-aspect SAR images based on improved GOFRO[J]. Journal of Radars, 2023, 12(5): 1081–1096. doi: 10.12000/JR23042

基于改进GOFRO的多角度SAR图像车辆目标检测方法

doi: 10.12000/JR23042
基金项目: 国家自然科学基金(61860206013)
详细信息
    作者简介:

    刘 琪,博士生,主要研究方向为目标检测、图像融合等

    禹卫东,博士,研究员,主要研究方向为SAR系统设计和研制、高分辨率SAR新体制、SAR成像处理和数据压缩等

    洪 文,博士,研究员,主要研究方向为多维度信号处理与信息提取、微波成像新概念新体制新方法等

    通讯作者:

    禹卫东 yuwd@aircas.ac.cn

  • 责任主编:殷君君 Corresponding Editor: YIN Junjun
  • 中图分类号: TN957.51

Vehicle Detection in Multi-aspect SAR Images Based on Improved GOFRO

Funds: The National Natural Science Foundation of China (61860206013)
More Information
  • 摘要: 针对城市场景中车辆目标分布状态随机,在检测过程中容易受到环境因素干扰等问题,提出一种将多角度合成孔径雷达(SAR)图像用于静止车辆目标提取的检测算法。在特征提取阶段,设计了一种适用于多角度图像上车辆目标的多尺度旋转不变的Gabor滤波器奇分量比例算子(MR-GOFRO)特征提取方法,对原有的GOFRO特征进行了滤波形式、特征尺度、特征方向、特征层次等4个方面的扩展,使其能够适应车辆目标在方向、尺度、形态等方面可能发生的变化。在图像融合阶段,设计了加权的非负矩阵分解(W-NMF)方法,根据特征质量调整来源于不同图像的特征权重,减少由于不同角度间相互干扰造成融合特征质量下降的现象。将该文所提出方法在不同的机载多角度图像数据集上进行验证,实验结果表明,该文提出的特征提取方法与同类方法相比,检测精度平均提升了3.69%;该文提出的特征融合方法与同类方法相比,检测精度提升了4.67%。

     

  • 图  1  多角度SAR图像中的车辆目标举例

    Figure  1.  Vehicle targets in multi-aspect SAR images

    图  2  多角度图像预处理流程图

    Figure  2.  Multi-aspect image preprocessing procedure

    图  3  多角度图像车辆目标检测流程图

    Figure  3.  Vehicle detection in multi-aspect image

    图  4  LoG和高斯滤波器在SAR图像上的滤波效果对比

    Figure  4.  Comparison of the filtering effects of LoG and Gaussian filter on SAR image

    图  5  GOFRO和MR-GOFRO方法的原理示意图

    Figure  5.  Schematic diagram of GOFRO and MR-GOFRO

    图  6  直线模型下多角度图像的成像几何

    Figure  6.  The imaging geometry of multi-aspect images under near-linear flight model

    图  7  阳江飞行实验现场图像

    Figure  7.  Images from the Yangjiang flight experiment

    图  8  待检测场景在城区中的具体分布位置展示

    Figure  8.  Locations of the detected scenes in urban area

    图  9  场景5在不同方位角度下的SAR图像

    Figure  9.  Vehicle targets in multi-aspect SAR images in scene 5

    图  10  真值图的生成过程示意图(以场景2为例)

    Figure  10.  The truth map generating process (Taking scene 2as an example)

    图  11  不同场景的真值图标注结果

    Figure  11.  Truth maps of different scenes

    图  12  不同场景相应的光学图像参考

    Figure  12.  Reference optical images of different scenes

    图  13  各场景中的检测结果

    Figure  13.  Detection results in different scenes

    图  14  舟山飞行实验数据集中不同场景的车辆目标检测结果

    Figure  14.  Vehicle detection results in different scenes in Zhoushan flight experiment dataset

    图  15  MR-GOFRO尺度缩放功能增加前后,同一场景中车辆目标的检测结果

    Figure  15.  The vehicle detection results in the same scene before and after the addition of MR-GOFRO scaling step

    图  16  MR-GOFRO方向调节功能增加前后,同一场景中车辆目标的检测结果

    Figure  16.  The vehicle detection results in the same scene before and after the addition of MR-GOFRO direction adjustment step

    图  17  MR-GOFRO纹理信息保留前后,同一场景中车辆目标的检测结果

    Figure  17.  The vehicle detection results in the same scene before and after retaining MR-GOFRO texture information

    图  18  场景2中使用不同检测方法所获取的实验结果

    Figure  18.  Detection results obtained by different methods in scene 2

    图  19  不同图像数量条件下的检测结果

    Figure  19.  Detection results under different image quantity conditions

    表  1  阳江飞行实验参数

    Table  1.   Yangjiang flight experiment parameters

    实验参数参数值
    中心频率9.6 GHz
    带宽3600 MHz
    脉宽15 μs
    采样频率4400 MHz
    脉冲重复频率3000 Hz
    中心角度65.5°
    平台速度83.04 m/s
    平台高度3605.44 m
    场景中心纬度21.88°
    场景中心经度111.97°
    图像分辨率1 m
    下载: 导出CSV

    表  2  阳江飞行实验多角度图像方位角度参数

    Table  2.   Aspect parameters in Yangjiang flight experiment

    序列号方位角度
    (°)
    角度间隔
    (°)
    角度范围
    (°)
    角度140.800
    角度232.68.28.2
    角度322.610.018.2
    角度411.011.629.8
    角度5011.040.8
    角度6–1.71.742.5
    角度7–14.112.454.9
    角度8–25.411.366.2
    角度9–34.99.575.7
    角度10–42.37.483.1
    下载: 导出CSV

    表  3  舟山飞行实验参数

    Table  3.   Zhoushan flight experiment parameters

    实验参数参数值
    中心频率9.6 GHz
    带宽1200 MHz
    脉宽20 μs
    采样频率1400 MHz
    脉冲重复频率3000 Hz
    中心角度55.0°
    平台高度7000 m
    平台速度137.34 m/s
    场景中心经度29.97°
    场景中心纬度122.29°
    图像分辨率0.7 m
    下载: 导出CSV

    表  4  舟山飞行实验多角度图像方位角度参数

    Table  4.   Aspect parameters of the multi-aspect images in Zhoushan flight experiment

    序列号方位角度(°)角度间隔(°)角度范围(°)
    角度140.400
    角度230.210.210.2
    角度320.59.719.9
    角度410.89.729.6
    角度50.410.440.0
    角度6–10.010.450.4
    角度7–19.89.860.2
    角度8–28.99.169.3
    角度9–39.210.379.6
    下载: 导出CSV

    表  5  实验中检测算法所选取的参数

    Table  5.   Detection experiment parameters

    实验参数参数值
    MR-GOFRO尺度12/15/19/24/30
    MR-GOFRO方向 $ \left[0,\pi /2\right] $
    $ \left[\pi /\mathrm{6,2}\pi /3\right] $
    $ [\pi /\mathrm{4,3}\pi /4] $
    $ [\pi /\mathrm{3,5}\pi /6] $
    NMF输出特征维数12
    车辆目标平均尺寸13×26
    检测窗口半径12
    车辆目标与检测窗口面积比0.6
    检测窗口步长12
    检测窗口采样点距离6
    检测窗口采样点数量5
    下载: 导出CSV

    表  6  阳江飞行实验数据集中不同场景车辆目标检测结果的衡量指标

    Table  6.   Indexes of vehicle detection results in different scenes in Yangjiang flight experiment dataset

    序列号精确率(%)准确率(%)漏警率(%)虚警率(%)
    场景185.4093.496.976.32
    场景272.4994.1111.395.07
    场景371.3697.578.002.11
    场景472.5096.485.071.76
    场景582.7989.6310.137.88
    平均值76.9094.258.314.63
    下载: 导出CSV

    表  7  舟山飞行实验数据集中不同场景车辆目标检测结果的衡量指标

    Table  7.   Indexes of vehicle detection results in different scenes in Zhoushan flight experiment dataset

    序列号精确率(%)准确率(%)漏警率(%)虚警率(%)
    场景688.2895.6419.833.43
    场景786.6294.9914.854.35
    平均值87.4595.3217.343.89
    下载: 导出CSV

    表  8  MR-GOFRO改进前后的检测结果衡量指标

    Table  8.   Indexes of detection results before and after the MR-GOFRO improvements

    检测
    方法
    精确率
    (%)
    准确率
    (%)
    漏警率
    (%)
    虚警率
    (%)
    GOFRO(场景1)85.4093.496.976.32
    GOFRO(场景7)86.6294.9914.854.53
    尺度缩放59.2183.7620.685.31
    方向调节61.0884.1330.424.29
    纹理信息66.1983.915.8620.44
    下载: 导出CSV

    表  9  不同方法检测结果的衡量指标

    Table  9.   Indexes of the detection results obtained by different methods

    检测
    方法
    精确率
    (%)
    准确率
    (%)
    漏警率
    (%)
    虚警率
    (%)
    方法166.9689.0028.508.31
    方法271.3793.0618.525.13
    方法360.8989.8921.188.29
    方法472.9284.5913.955.64
    方法568.2890.019.1510.11
    本文方法79.9194.5610.894.42
    下载: 导出CSV

    表  10  不同图像数量条件下的检测结果衡量指标

    Table  10.   Indexes of detection results under different image quantity conditions

    图像
    数量
    精确率
    (%)
    准确率
    (%)
    漏警率
    (%)
    虚警率
    (%)
    处理
    时间(s)
    180.9082.5643.175.989.08
    275.8384.0230.569.6362.28
    480.2089.7211.6710.56100.81
    682.7989.6310.137.88146.98
    下载: 导出CSV
  • [1] LEITLOFF J, HINZ S, and STILLA U. Vehicle detection in very high resolution satellite images of city areas[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(7): 2795–2806. doi: 10.1109/TGRS.2010.2043109
    [2] PALUBINSKAS G and RUNGE H. Change detection for traffic monitoring in TerraSAR-X imagery[C]. 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, USA, 2008: I-169–I-172,
    [3] MITTERMAYER J, WOLLSTADT S, PRATS-IRAOLA P, et al. The TerraSAR-X staring spotlight mode concept[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(6): 3695–3706. doi: 10.1109/TGRS.2013.2274821
    [4] ZOU Bin, QIN Jiang, and ZHANG Lamei. Vehicle detection based on semantic-context enhancement for high-resolution SAR images in complex background[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4503905. doi: 10.1109/LGRS.2021.3139605
    [5] MAKSYMIUK O, SCHMITT M, BRENNER A R, et al. First investigations on detection of stationary vehicles in airborne decimeter resolution SAR data by supervised learning[C]. 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 2012: 3584–3587.
    [6] BAUMGARTNER S V and KRIEGER G. Real-time road traffic monitoring using a fast a priori knowledge based SAR-GMTI algorithm[C]. 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, USA, 2010: 1843–1846.
    [7] NOVAK L M, OWIRKA G J, and BROWER W S. Performance of 10- and 20-target MSE classifiers[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(4): 1279–1289. doi: 10.1109/7.892675
    [8] EL-DARYMLI K, GILL E W, MCGUIRE P, et al. Automatic target recognition in synthetic aperture radar imagery: A state-of-the-art review[J]. IEEE Access, 2016, 4: 6014–6058. doi: 10.1109/ACCESS.2016.2611492
    [9] CHENG Gong and HAN Junwei. A survey on object detection in optical remote sensing images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 117: 11–28. doi: 10.1016/j.isprsjprs.2016.03.014
    [10] WANG Zhixu, XIN Zhihui, HUANG Xiaoqiao, et al. Overview of SAR Image Feature Extraction and Target Recognition[M]. JAIN L C, KOUNTCHEV R, and SHI Junsheng. 3D Imaging Technologies—Multi-dimensional Signal Processing and Deep Learning. Singapore: Springer, 2021: 69–75.
    [11] LI Lu, DU Yuang, and DU Lan. Vehicle target detection network in SAR images based on rectangle-invariant rotatable convolution[J]. Remote Sensing, 2022, 14(13): 3086. doi: 10.3390/rs14133086
    [12] YANG Xinpeng, ZHANG Qiang, ZHAO Shixiang, et al. Focal-pyramid-based vehicle segmentation in SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4028705. doi: 10.1109/LGRS.2022.3224904
    [13] BRENNER A R, ESSEN H, and STILLA U. Representation of stationary vehicles in ultra-high resolution SAR and turntable ISAR images[C]. The 9th European Conference on Synthetic Aperture Radar, Nuremberg, Germany, 2012: 147–150.
    [14] WANG Guoli, WANG Xinchao, FAN Bin, et al. Feature extraction by rotation-invariant matrix representation for object detection in aerial image[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(6): 851–855. doi: 10.1109/LGRS.2017.2683495
    [15] SUN Yi, WANG Wenna, ZHANG Qianyu, et al. Improved YOLOv5 with transformer for large scene military vehicle detection on SAR image[C]. The 2022 7th International Conference on Image, Vision and Computing, Xi’an, China, 2022: 87–93.
    [16] 龙泓琳, 皮亦鸣, 曹宗杰. 基于非负矩阵分解的SAR图像目标识别[J]. 电子学报, 2010, 38(6): 1425–1429.

    LONG Honglin, PI Yiming, and CAO Zongjie. Non-negative matrix factorization for target recognition[J]. Acta Electronica Sinica, 2010, 38(6): 1425–1429.
    [17] ZHANG Haichao, NASRABADI N M, ZHANG Yanning, et al. Multi-view automatic target recognition using joint sparse representation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(3): 2481–2497. doi: 10.1109/TAES.2012.6237604
    [18] MA Wenping, WEN Zelian, WU Yue, et al. Remote sensing image registration with modified SIFT and enhanced feature matching[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(1): 3–7. doi: 10.1109/LGRS.2016.2600858
    [19] XIANG Yuming, WANG Feng, WAN Ling, et al. An advanced multiscale edge detector based on Gabor filters for SAR imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(9): 1522–1526. doi: 10.1109/LGRS.2017.2720684
    [20] PAUL S and PATI U C. A Gabor odd filter-based ratio operator for SAR image matching[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(3): 397–401. doi: 10.1109/LGRS.2018.2872979
    [21] 张之光, 雷宏. 基于SAR图像样本的本征维数检测人造目标[J]. 电子测量技术, 2016, 39(9): 34–39. doi: 10.3969/j.issn.1002-7300.2016.09.009

    ZHANG Zhiguang and LEI Hong. Man-made targets detection based on intrinsic dimension of SAR image samples[J]. Electronic Measurement Technology, 2016, 39(9): 34–39. doi: 10.3969/j.issn.1002-7300.2016.09.009
    [22] 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
    [23] OLUKANMI P O and TWALA B. K-means-sharp: Modified centroid update for outlier-robust k-means clustering[C]. 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics, Bloemfontein, South Africa, 2017: 14–19,
    [24] WU Xin, HONG Danfeng, TIAN Jiaojiao, et al. ORSIm detector: A novel object detection framework in optical remote sensing imagery using spatial-frequency channel features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 57(7): 5146–5158. doi: 10.1109/TGRS.2019.2897139
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出版历程
  • 收稿日期:  2023-04-10
  • 修回日期:  2023-05-14
  • 网络出版日期:  2023-06-20
  • 刊出日期:  2023-10-28

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