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SAR目标增量识别中基于最大化非重合体积的样例挑选方法

李斌 崔宗勇 汪浩瀚 周正 田宇 曹宗杰

李斌, 崔宗勇, 汪浩瀚, 周正, 田宇, 曹宗杰. SAR目标增量识别中基于最大化非重合体积的样例挑选方法[J]. 电子与信息学报, 2024, 46(10): 3918-3927. doi: 10.11999/JEIT240217
引用本文: 李斌, 崔宗勇, 汪浩瀚, 周正, 田宇, 曹宗杰. SAR目标增量识别中基于最大化非重合体积的样例挑选方法[J]. 电子与信息学报, 2024, 46(10): 3918-3927. doi: 10.11999/JEIT240217
LI Bin, CUI Zongyong, WANG Haohan, ZHOU Zheng, TIAN Yu, CAO Zongjie. Exemplar Selection Based on Maximizing Non-overlapping Volume in SAR Target Incremental Recognition[J]. Journal of Electronics & Information Technology, 2024, 46(10): 3918-3927. doi: 10.11999/JEIT240217
Citation: LI Bin, CUI Zongyong, WANG Haohan, ZHOU Zheng, TIAN Yu, CAO Zongjie. Exemplar Selection Based on Maximizing Non-overlapping Volume in SAR Target Incremental Recognition[J]. Journal of Electronics & Information Technology, 2024, 46(10): 3918-3927. doi: 10.11999/JEIT240217

SAR目标增量识别中基于最大化非重合体积的样例挑选方法

doi: 10.11999/JEIT240217
基金项目: 国家自然科学基金(62271116)
详细信息
    作者简介:

    李斌:男,博士生,研究方向为SAR目标增量学习

    崔宗勇:男,博士,研究方向为SAR目标检测

    汪浩瀚:女,硕士生,研究方向为SAR目标增量学习

    周正:男,博士生,研究方向为小样本SAR目标检测

    田宇:男,博士生,研究方向为增量SAR目标检测

    曹宗杰:男,博士,研究方向为SAR目标成像与识别

    通讯作者:

    曹宗杰 zjcao@uestc.edu.cn

  • 中图分类号: TN957.52

Exemplar Selection Based on Maximizing Non-overlapping Volume in SAR Target Incremental Recognition

Funds: The National Natural Science Foundation of China (62271116)
  • 摘要: 为了确保合成孔径雷达(SAR)自动目标识别(ATR)系统能够迅速适应新的应用环境,其必须具备快速学习新类的能力。目前的SAR ATR系统在学习新类时需要不断重复训练所有旧类样本,这会造成大量存储资源的浪费,同时识别模型无法快速更新。保留少量的旧类样例进行后续的增量训练是模型增量识别的关键。为了解决这个问题,该文提出基于最大化非重合体积的样例挑选方法(ESMNV),一种侧重于分布非重合体积的样例选择算法。ESMNV将每个已知类的样例选择问题转化为分布非重合体积的渐近增长问题,旨在最大化所选样例的分布的非重合体积。ESMNV利用分布之间的相似性来表示体积之间的差异。首先,ESMNV使用核函数将目标类别的分布映射到重建核希尔伯特空间(RKHS),并使用高阶矩来表示分布。然后,它使用最大均值差异(MMD)来计算目标类别与所选样例分布之间的差异。最后,结合贪心算法,ESMNV逐步选择使样例分布与目标类别分布差异最小的样例,确保在有限数量的样例情况下最大化所选样例的非重合体积。
  • 图  1  所选样例分布q与候选样本分布$ e $之间的3种关系类型

    图  2  所选样例非重合体积的示意图

    图  3  ESMNV在MSTAR数据集类2S1上的示例性选择可视化结果

    图  4  所选样例分布的进一步分析

    图  5  各方法在不同初始训练类数N下的平均增量准确度

    图  6  各方法在不同增量步长T条件下的平均增量准确率

    图  7  各方法在SAR-AIRcraft-1.0上不同保存样例m条件下的平均增量准确度

    图  8  各方法在OpenSARShip上不同保存样例m条件下的平均增量准确率

    表  1  保留样例数量为5时的增量识别准确率(%)

    模型45678910
    CBesIL99.6279.9667.2358.4453.8847.9341.88
    Random99.6283.3269.9861.5656.7349.9647.21
    Herding99.6283.9271.7761.9558.6752.1651.04
    DCBES99.6283.0971.9065.0759.5455.2754.42
    ESMNV99.6287.1575.6069.3266.9561.9060.49
    下载: 导出CSV

    表  2  保留样例数量为10时的增量识别准确率(%)

    模型45678910
    CBesIL99.6289.1380.1074.4470.5868.6365.31
    Random99.6290.0980.4678.0573.2570.3868.68
    Herding99.6289.6883.6479.0876.2074.3371.10
    DCBES99.6291.3385.0881.6779.1277.6975.97
    ESMNV99.6292.8786.0385.4083.5381.6281.05
    下载: 导出CSV

    表  3  保留样例数量为15时的增量识别准确率(%)

    模型45678910
    CBesIL99.6292.2585.7683.2180.5179.0475.35
    Random99.6293.0087.0686.2884.1980.6479.70
    Herding99.6293.4888.2486.3785.7083.0181.10
    DCBES99.6293.6389.4288.6587.0285.0983.70
    ESMNV99.6295.5392.1590.5989.7389.2887.31
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-03-28
  • 修回日期:  2024-08-21
  • 网络出版日期:  2024-08-30
  • 刊出日期:  2024-10-30

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