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基于稀疏表示的低照度遥感影像夜间海上船舶灯光检测方法

薛成宬 高彩霞 胡坚 邱实 汪琪

薛成宬, 高彩霞, 胡坚, 邱实, 汪琪. 基于稀疏表示的低照度遥感影像夜间海上船舶灯光检测方法[J]. 电子与信息学报, 2023, 45(5): 1747-1757. doi: 10.11999/JEIT220369
引用本文: 薛成宬, 高彩霞, 胡坚, 邱实, 汪琪. 基于稀疏表示的低照度遥感影像夜间海上船舶灯光检测方法[J]. 电子与信息学报, 2023, 45(5): 1747-1757. doi: 10.11999/JEIT220369
XUE Chengcheng, GAO Caixia, HU Jian, QIU Shi, WANG Qi. Marine Boat Lights Detection Method Based on Sparse Representation for Low Light Remote Sensing Images During Night[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1747-1757. doi: 10.11999/JEIT220369
Citation: XUE Chengcheng, GAO Caixia, HU Jian, QIU Shi, WANG Qi. Marine Boat Lights Detection Method Based on Sparse Representation for Low Light Remote Sensing Images During Night[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1747-1757. doi: 10.11999/JEIT220369

基于稀疏表示的低照度遥感影像夜间海上船舶灯光检测方法

doi: 10.11999/JEIT220369
基金项目: 国家重点研发计划(2018YFB0504600),中国科学院前沿科学重点研发项目(QYZDB-SSW-JSC051)
详细信息
    作者简介:

    薛成宬:女,博士生,研究方向为低照度遥感数据处理、目标检测

    高彩霞:女,研究员,研究方向为定量遥感

    胡坚:男,研究员,研究方向为遥感载荷数据处理和应用

    邱实:女,研究员,研究方向为低照度遥感在轨定标、低照度遥感辐射产品应用

    汪琪:男,副研究员,研究方向为遥感图像处理

    通讯作者:

    胡坚 jianhu_cas@163.com

  • 中图分类号: TN911.73; TP751

Marine Boat Lights Detection Method Based on Sparse Representation for Low Light Remote Sensing Images During Night

Funds: The National Key Research and Development Program of China (2018YFB0504600), The Key Research Program of Frontier Sciences of Chinese Academy of Sciences (QYZDB-SSW-JSC051)
  • 摘要: 针对目前基于低照度遥感影像对夜间海上船舶检测存在的目标特征挖掘不足的问题,该文设计了一种可使船舶目标样本和背景噪声样本最小错分的稀疏度指标,提出一种基于稀疏编码算法和字典学习算法的低照度遥感影像夜间船舶灯光检测算法,并将其应用于墨西哥湾北部海域、天津港南侧海域和上海港东侧海域,检测精确度分别为96.36%, 95.12%, 86.26%,召回率分别为96.36%, 92.86%, 94.19%,调和平均值分别为96.36%, 93.98%, 90.05%;进一步地,该文将此算法与3种典型低照度遥感影像夜间海上船舶检测算法进行了对比分析,结果表明该文算法更具有优越性能,可为夜间海上船舶的检测提供新的思路。
  • 图  1  检测算法流程图

    图  2  船舶灯光样本和背景噪声样本的稀疏度指标计算结果统计直方图

    图  3  船舶灯光样本和背景噪声样本的新指标计算结果统计直方图

    图  4  本文算法在验证集上的$ P - R $曲线

    图  5  本文算法在3个不同海域的检测结果

    图  6  本文算法在3个不同海域的检测结果与船舶灯光实际位置的对比

    图  7  已有的低照度遥感影像夜间海上船舶灯光检测算法与本文算法的$ P - R $曲线对比图

    图  8  已有的低照度遥感影像夜间海上船舶灯光检测算法在墨西哥湾北部海域的检测结果

    图  9  已有的低照度遥感影像夜间海上船舶灯光检测算法在天津港南侧海域的检测结果

    图  10  已有的低照度遥感影像夜间海上船舶灯光检测算法在上海港东侧海域的检测结果

    表  1  常用的稀疏度指标及其计算公式

    稀疏度指标计算公式
    $ {l_p} $范数$ {l_p} = {\left( {\displaystyle\sum\limits_{i = 1}^N {{{\left| {{x_i}} \right|}^p}} } \right)^{{1 \mathord{\left/ {\vphantom {1 p}} \right. } p}}},0 < p \le 1 $(4)
    $ {l_2} $范数与$ {l_1} $范数之比$ {{{l_2}} \mathord{\left/ {\vphantom {{{l_2}} {{l_1}}}} \right. } {{l_1}}} = {{\sqrt {\displaystyle\sum\limits_{i = 1}^N {x_i^2} } } \mathord{\left/ {\vphantom {{\sqrt {\displaystyle\sum\limits_{i = 1}^N {x_i^2} } } {\displaystyle\sum\limits_{i = 1}^N {\left| {{x_i}} \right|} }}} \right. } {\displaystyle\sum\limits_{i = 1}^N {\left| {{x_i}} \right|} }} $(5)
    Hoyer系数${\rm{Hoyer} } = { {\left( {\sqrt N - \dfrac{ { {l_1} } }{ { {l_2} } } } \right)} \mathord{\left/ {\vphantom { {\left( {\sqrt N - \frac{ { {l_1} } }{ { {l_2} } } } \right)} {\left( {\sqrt N - 1} \right)} } } \right. } {\left( {\sqrt N - 1} \right)} }$(6)
    Gini系数${\rm{Gini} } = 1 - \dfrac{2}{ { {l_1} } }\displaystyle\sum\limits_{i = 1}^N { {x_i} } \frac{ {N - i + 1/2} }{N},|{x_1}| \le |{x_2}| \le \cdots \le \left| { {x_N} } \right|$(7)
    下载: 导出CSV

    表  2  各稀疏度指标对船舶灯光和背景噪声的区分度

    稀疏度指标$ {\rho _{{\text{target}}}} $${\rho _{ {{\rm{noise}}} } }$$ {J_{\min }} $
    $ {l_0} $范数1.001.0048.00
    $ {l_1} $范数0.390.6632.43
    $ {l_{{1 \mathord{\left/ {\vphantom {1 2}} \right. } 2}}} $范数0.340.4312.01
    $ {{{l_2}} \mathord{\left/ {\vphantom {{{l_2}} {{l_1}}}} \right. } {{l_1}}} $0.400.802.76
    Hoyer系数0.400.807.72
    Gini系数0.981.004.57
    下载: 导出CSV

    表  3  本文算法在3个不同海域的检测性能评价指标值

    研究区域$ P $$ R $${\rm{F} }1$
    墨西哥湾北部海域0.96360.96360.9636
    天津港南侧海域0.95120.92860.9398
    上海港东侧海域0.86260.94190.9005
    下载: 导出CSV

    表  4  已有的低照度遥感影像夜间海上船舶灯光检测算法和本文算法在验证集和测试集上的检测性能评价指标值

    算法验证集测试集
    $ P $$ R $${\rm{F}}1$$ P $$ R $$ {\rm{F}}1 $
    基于SMI特征的检测算法0.93270.90000.91610.89510.90260.8988
    基于辐亮度梯度特征的检测算法0.93420.91500.92450.90480.90130.9030
    CFAR检测算法0.91190.87500.89310.86990.87640.8731
    本文算法0.95850.97500.96670.97310.94760.9602
    下载: 导出CSV

    表  5  已有的低照度遥感影像夜间海上船舶灯光检测算法在3个不同海域的检测性能评价指标值

    算法墨西哥湾北部海域天津港南侧海域上海港东侧海域
    $ P $$ R $$ {\rm{F}}1 $$ P $$ R $$ {\rm{F}}1 $$ P $$ R $$ {\rm{F}}1 $
    基于SMI特征的检测算法0.83640.92000.87620.92500.88100.90250.86590.82560.8453
    基于辐亮度梯度特征的检测算法0.73910.68000.70830.72090.73810.72940.79550.81400.8046
    CFAR检测算法0.77270.68000.72340.80950.80950.80950.82560.82560.8256
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-31
  • 修回日期:  2022-06-20
  • 网络出版日期:  2022-06-30
  • 刊出日期:  2023-05-10

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