基于视觉显著性的SAR遥感图像NanoDet舰船检测方法

刘方坚 李媛

刘方坚, 李媛. 基于视觉显著性的SAR遥感图像NanoDet舰船检测方法[J]. 雷达学报, 2021, 10(6): 885–894. doi: 10.12000/JR21105
引用本文: 刘方坚, 李媛. 基于视觉显著性的SAR遥感图像NanoDet舰船检测方法[J]. 雷达学报, 2021, 10(6): 885–894. doi: 10.12000/JR21105
LIU Fangjian and LI Yuan. SAR remote sensing image ship detection method NanoDet based on visual saliency[J]. Journal of Radars, 2021, 10(6): 885–894. doi: 10.12000/JR21105
Citation: LIU Fangjian and LI Yuan. SAR remote sensing image ship detection method NanoDet based on visual saliency[J]. Journal of Radars, 2021, 10(6): 885–894. doi: 10.12000/JR21105

基于视觉显著性的SAR遥感图像NanoDet舰船检测方法

DOI: 10.12000/JR21105
基金项目: 国家自然科学基金(61972021, 61672076)
详细信息
    作者简介:

    刘方坚(1979–),男,山东临沂人,中国科学院空天信息创新研究院副研究员,主要研究方向为遥感卫星地面处理系统技术研究等

    李 媛(1996–),女,河北石家庄人,于北京化工大学获得学士、硕士学位,现为北京理工大学博士生,主要研究方向为遥感图像分类、目标检测等

    通讯作者:

    刘方坚 liufj@aircas.ac.cn

  • 责任主编:孙显 Corresponding Editor: SUN Xian
  • 中图分类号: TN957.52

SAR Remote Sensing Image Ship Detection Method NanoDet Based on Visual Saliency

Funds: The National Natural Science Foundation of China (61972021, 61672076)
More Information
  • 摘要: 在合成孔径雷达遥感图像中,舰船由金属材质构成,后向散射强;海面平滑,后向散射弱,因此舰船是海面背景下的视觉显著目标。然而,SAR遥感影像幅宽大、海面背景复杂,且不同舰船目标特征差异大,导致舰船快速准确检测困难。为此,该文提出一种基于视觉显著性的SAR遥感图像NanoDet舰船检测方法。该方法首先通过自动聚类算法划分图像样本为不同场景类别;其次,针对不同场景下的图像进行差异化的显著性检测;最后,使用优化后的轻量化网络模型NanoDet对加入显著性图的训练样本进行特征学习,使系统模型能够实现快速和高精确度的舰船检测效果。该方法对SAR图像应用实时性具有一定的帮助,且其轻量化模型利于未来实现硬件移植。该文利用公开数据集SSDD和AIR-SARship-2.0进行实验验证,体现了该算法的有效性。

     

  • 图  1  系统整体流程示意图

    Figure  1.  Schematic diagram of the overall system process

    图  2  极小PAN结构

    Figure  2.  Structure diagram of minimize PAN

    图  3  FCOS检测头结构图

    Figure  3.  Structure diagram of FCOS detection head

    图  4  多尺度特征提取示意图

    Figure  4.  Schematic diagram of multi-scale feature extraction

    图  5  添加预处理效果图

    Figure  5.  The result diagram of the preprocessing module

    图  6  是否加入本文预处理方法检测结果对比图

    Figure  6.  The result diagram of the ship detection method with the preprocessing module added or not

    图  7  本文方法基于SSDD数据集的检测结果图

    Figure  7.  The ship inspection result diagram based on SSDD dataset of the method proposed in this paper

    图  8  本文方法基于AIR-SARship数据集的检测结果图

    Figure  8.  The detection result diagram based on AIR-SARship dataset of our approach

    图  9  不同方法检测结果对比图

    Figure  9.  Comparison chart of detection results of different methods

    图  10  不同方法精度-召回率曲线

    Figure  10.  Precision-Recall curves of different methods.

    表  1  基于全部实验数据的不同方法检测性能对比

    Table  1.   Comparison of detection performance of different methods based on all dataset

    方法PD (%)PF (%)Precision (%)Recall (%)mAP (%)Time (ms)
    Yolov2-tiny53.1014.9775.3653.1055.209.37
    Yolov3-tiny71.0313.5576.1571.0361.4211.65
    NanoDet83.5610.0383.6483.5676.334.96
    Faster R-CNN84.989.6282.3784.9874.94313.55
    SSD94.367.9685.8392.3682.5253.98
    Yolov494.984.6689.3493.9888.62120.34
    Yolov595.125.4394.6895.1290.5637.52
    PANet93.224.5294.9693.2290.77183.68
    未加预处理方法92.655.5386.7192.6585.895.39
    本文方法95.643.4895.4795.6492.495.22
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出版历程
  • 收稿日期:  2021-07-22
  • 修回日期:  2021-09-18
  • 网络出版日期:  2021-10-19
  • 刊出日期:  2021-12-28

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