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基于双边截断的双参数海上风电站SAR图像CFAR检测

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文章提出了1种基于双边截断的双参数海上风电站SAR图像CFAR检测器DTCS-TPCFAR,目的是提高在具有多个目标海上区域和石油泄漏区域等环境下对海上风电站的检测性能.DTCS-TPCFAR所提出的双边截断杂波的方法,能够同时消除高强度和低强度异常值的干扰,同时保留真实的杂波样本.通过使用最大似然估计计算双边截断后样本的均值和标准差,然后通过这2个参数估计值计算出截断阈值,最后再结合指定的虚警率(Probability of False Alarm,PFA)来对测试单元(Test Cell,TC)进行判断,完成最终的目标检测.这也是首次将CFAR检测器用于检测海上风电站.文章通过Sentinel-1数据集来验证该方法的有效性.实验结果表明,文章所提出的算法在相同指定虚警率下,具有更高的检测率(Detection Rate,DR)和更低的误报率(False Alarm Rate,FAR).
Two-Parameter CFAR Detectionin of Offshore Wind Farms SAR Images Based on Dual Truncated Clutter Statistics
A dual-truncated-clutter-statistics two-parameter CFAR(DTCS-TPCFAR)detector for offshore wind farms in SAR images is proposed.The aim of DTCS-TPCFAR is to improve the detection performance of offshore wind farms in environments such as complex areas with multiple targets and oil spill areas.The proposed method of dual-truncated clut-ter in DTCS-TPCFAR can simultaneously eliminate interference from both high-intensity and low-intensity outliers while preserving true clutter samples.By using maximum likelihood estimation to calculate the mean and standard deviation of the truncated samples,the truncation threshold is computed based on these two estimated parameters.Finally,the target detection is accomplished by detecting the test cells(TC)using the specified probability of false alarm(PFA).This is the first time that CFAR detectors have been applied to detect offshore wind farms.The effectiveness of this method is vali-dated using the Sentinel-1 dataset.Experimental results demonstrate that the proposed algorithm achieves higher detection rate(DR)and lower false alarm rate(FAR)at the same specified PFA compared to other CFAR detectors.

SAR imagesoffshore wind farms detectionconstant false alarm rate(CFAR)detectioncomplex environ-mentdual-truncated clutter statistics

余佳恒、艾加秋、史骏、张勇

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合肥工业大学计算机与信息学院,安徽 合肥 230009

安徽大学信息材料与智能感知安徽省实验室,安徽 合肥 230601

合肥工业大学智能互联系统安徽省实验室,安徽 合肥 230009

合肥工业大学软件学院,安徽 合肥 230009

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SAR图像 海上风电站检测 恒虚警率检测 复杂环境 双边截断杂波统计特性

国家自然科学基金面上项目合肥市自然科学基金信息材料与智能感知安徽省实验室开放课题信息材料与智能感知安徽省实验室开放课题智能互联系统安徽省实验室开放课题

620711642022001IMIS202102IMIS202214PA2023IISL0098

2024

海军航空大学学报
海军航空工程学院科研部

海军航空大学学报

CSTPCD
影响因子:0.279
ISSN:
年,卷(期):2024.39(2)
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