Fast detection method for low false alarm of multi-channel spaceborne SAR image combined with confidence calculation

Runan ZHENG; Xiaolan QIU

doi:10.11834/jrs.20219336

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Fast detection method for low false alarm of multi-channel spaceborne SAR image combined with confidence calculation
Vol. 26, Issue 3, Pages: 516-527(2022)
Received：23 September 2019，

Published：07 March 2022
DOI： 10.11834/jrs.20219336
稿件说明：

移动端阅览

郑儒楠，仇晓兰.2022.结合置信度的多通道星载SAR在轨舰船低虚警快速检测.遥感学报，26（3）： 516-527 DOI： 10.11834/jrs.20219336.

Zheng R N and Qiu X L. 2022. Fast detection method for low false alarm of multi-channel spaceborne SAR image combined with confidence calculation. National Remote Sensing Bulletin， 26（3）：516-527 DOI： 10.11834/jrs.20219336.

摘要

舰船目标快速检测是星载合成孔径雷达的重要应用之一，也是SAR星上处理首先发展的应用方向。目前SAR舰船快速检测普遍使用的图像恒虚警（CFAR）检测方法中，低虚警一直是研究的重点，多通道SAR中舰船目标存在多通道虚假目标，进一步增加了虚警剔除的难度。为此，本文提出了一种基于置信度计算和SVM判别的低虚警CFAR（Constant False-Alarm Rate）舰船快速检测方法。首先，在传统CFAR算法中引入分块计算，同时针对目前舰船的长宽、面积等几何参数统计，设计舰船目标置信度计算方法，并对几何参数相近的虚假目标利用SVM分类器进行舰船目标与虚假目标的快速区分，并设计了CFAR检测总体流程。最后，采用GF-3卫星双通道模式实测数据对已有方法和新方法进行了算法运行时间及目标检测性能对比。实验结果表明，在基于TX2构建的星上处理平台上，新方法运行时间比传统CFAR方法缩短30倍，具有良好的实时性，同时对舰船目标检测检测率达到97.8%的同时，虚警率控制在5.2%，具有良好的虚假目标剔除效果。

Abstract

The rapid detection of ship targets is one of the important applications of spaceborne synthetic aperture radar (SAR) and the first application direction of SAR on-board processing. At present

the constant false alarm (CFAR) detection method is the most commonly used in the rapid detection of SAR ships

and low false alarm has always been the focus of research. In multi-channel SAR

ship targets have multi-channel false targets

further increasing the difficulty of false alarm elimination.

This paper proposes a fast detection method for low false alarm CFAR ships according to confidence calculation and SVM discrimination. First

the traditional CFAR algorithm introduces block calculation while designing the calculation method of ship target confidence and at the same time

according to the statistics of the geometric parameters of the current ship’s length

width

area

and so on

and the SVM classifier is used for the false targets with similar geometric parameters. Quickly distinguishing between ship and false targets and designing the overall process of CFAR detection. Finally

the GF-3 satellite dual-channel mode measured data is used to compare the algorithm running time and the target detection performance between the existing and new methods.

Experimental results show that on the on-board processing platform based on TX2

the new method runs 30 times shorter than the traditional CFAR method and has good real-time performance. Meanwhile

the detection rate of ship targets reaches 97.8%

and the false alarm rate is controlled at 5.2%

showing a good ability of eliminating false targets.

In view of the low real-time performance of traditional CFAR on spaceborne platforms and the existence of multiple false targets in the detection results

a method for quickly calculating the CFAR panoramic threshold is proposed. Experiments show that this algorithm has good real-time and detection performance for the ship detection problem of multi-channel spaceborne SAR data and can quickly and effectively detect ship targets and remove most false targets.

关键词

Keywords

references

Amoon M , Bozorgi A and Rezai-Rad G A . 2013 . New method for ship detection in synthetic aperture radar imagery based on the human visual attention system . Journal of Applied Remote Sensing , 7 ( 1 ): 071599 [ DOI: 10.1117/1.jrs.7.071599 http://dx.doi.org/10.1117/1.jrs.7.071599 ]

Brekke C and Anfinsen S N . 2011 . Ship detection in ice-infested waters based on dual-polarization SAR imagery . IEEE Geoscience and Remote Sensing Letters , 8 ( 3 ): 391 - 395 [ DOI: 10.1109/lgrs.2010.2078796 http://dx.doi.org/10.1109/lgrs.2010.2078796 ]

Dalal N and Triggs B . 2005 . Histograms of oriented gradients for human detection // 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) . San Diego : IEEE: 886 - 893 [ DOI: 10.1109/CVPR.2005.177 http://dx.doi.org/10.1109/CVPR.2005.177 ]

Demirhan M E and Salor Ö . 2016 . Classification of targets in SAR images using SVM and k-NN techniques // 2016 24th Signal Processing and Communication Application Conference (SIU) . Zonguldak : IEEE: 1581 - 1584 [ DOI: 10.1109/SIU.2016.7496056 http://dx.doi.org/10.1109/SIU.2016.7496056 ]

El-Darymli K , McGuire P , Power D and Moloney C R . 2013 . Target detection in synthetic aperture radar imagery: a state-of-the-art survey . Journal of Applied Remote Sensing , 7 ( 1 ): 071598 [ DOI: 10.1117/1.JRS.7.071598 http://dx.doi.org/10.1117/1.JRS.7.071598 ]

Engen G , Larsen Y and Johnsen H . 2007 . Super-resolution of SAR data for ship detection . KSAT/Contract No : 06 -10173-A-C. Norut Informasjonsteknologi AS

Ferrara G , Migliaccio M , Nunziata F and Sorrentino A . 2011 . Generalized-K (GK)-based observation of metallic objects at sea in full-resolution Synthetic Aperture Radar (SAR) data: a multipolarization study . IEEE Journal of Oceanic Engineering , 36 ( 2 ): 195 - 204 [ DOI: 10.1109/joe.2011.2109491 http://dx.doi.org/10.1109/joe.2011.2109491 ]

Gambardella A , Nunziata F and Migliaccio M . 2008 . A physical full-resolution SAR ship detection filter . IEEE Geoscience and Remote Sensing Letters , 5 ( 4 ): 760 - 763 [ DOI: 10.1109/lgrs.2008.2005255 http://dx.doi.org/10.1109/lgrs.2008.2005255 ]

Huang S Q , Liu D Z , Gao G Q and Guo X J . 2009 . A novel method for speckle noise reduction and ship target detection in SAR images . Pattern Recognition , 42 ( 7 ): 1533 - 1542 [ DOI: 10.1016/j.patcog.2009.01.013 http://dx.doi.org/10.1016/j.patcog.2009.01.013 ]

Hwang S I and Ouchi K . 2010 . On a novel approach using MLCC and CFAR for the improvement of ship detection by synthetic aperture radar . IEEE Geoscience and Remote Sensing Letters , 7 ( 2 ): 391 - 395 [ DOI: 10.1109/lgrs.2009.2037341 http://dx.doi.org/10.1109/lgrs.2009.2037341 ]

Kadyrov A , Yu H and Liu H H . 2013 . Ship detection and segmentation using image correlation // 2013 IEEE International Conference on Systems, Man, and Cybernetics . Manchester : IEEE: 3119 - 3126 [ DOI: 10.1109/SMC.2013.532 http://dx.doi.org/10.1109/SMC.2013.532 ]

Leng X G , Ji K F , Fan Q J , Zhou S L and Zou H X . 2016 . A novel adaptive ship detection method for spaceborne SAR imagery // 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) . Beijing : IEEE: 108 - 111 [ DOI: 10.1109/IGARSS.2016.7729018 http://dx.doi.org/10.1109/IGARSS.2016.7729018 ]

Leung H , Dubash N and Xie N . 2002 . Detection of small objects in clutter using a GA-RBF neural network . IEEE Transactions on Aerospace and Electronic Systems , 38 ( 1 ): 98 - 118 [ DOI: 10.1109/7.993232 http://dx.doi.org/10.1109/7.993232 ]

Nan J , Wang C , Zhang B , Wu F , Zhang H and Tang Y X . 2013 . Ship wake CFAR detection algorithm in SAR images based on length normalized scan // 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS . Melbourne : IEEE: 3562 - 3565 [ DOI: 10.1109/IGARSS.2013.6723599 http://dx.doi.org/10.1109/IGARSS.2013.6723599 ]

Sciotti M , Pastina D and Lombardo P . 2002 . Exploiting the polarimetric information for the detection of ship targets in non-homogeneous SAR images // IEEE International Geoscience and Remote Sensing Symposium . Toronto : IEEE: 1911 - 1913 [ DOI: 10.1109/IGARSS.2002.1026297 http://dx.doi.org/10.1109/IGARSS.2002.1026297 ]

Tao Y H , Lang H T and Shi H J . 2018 . A new scattering similarity based metric for ship detection in Pol-SAR image //IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia : IEEE: 9331 - 9334 [ DOI: 10.1109/IGARSS.2018.8651433 http://dx.doi.org/10.1109/IGARSS.2018.8651433 ]

Tello M , Lopez-Martinez C , Mallorqui J and Bonastre R . 2006 . Automatic detection of spots and extraction of frontiers in SAR images by means of the wavelet transform: application to ship and coastline detection // 2006 IEEE International Symposium on Geoscience and Remote Sensing . Denver : IEEE: 383 - 386 [ DOI: 10.1109/IGARSS.2006.103 http://dx.doi.org/10.1109/IGARSS.2006.103 ]

Tu F , Kang C Y , Yin S and He C . 2015 . The ship detection of SAR image using CFAR based on mixture models chosen adaptively . Journal of Signal Processing , 31 ( 12 ): 1665 - 1673

涂峰 , 康陈瑶 , 尹莎 , 何楚 . 2015 . 自选择混合分布模型的CFAR用于SAR图像舰船检测 . 信号处理 , 31 ( 12 ): 1665- 1673 [ DOI: 10.3969/j.issn.1003-0530.2015.12.020 http://dx.doi.org/10.3969/j.issn.1003-0530.2015.12.020 ]

Wackerman C C , Friedman K S , Pichel W G , Clemente-Colón P and Li X . 2001 . Automatic detection of ships in RADARSAT-1 SAR imagery . Canadian Journal of Remote Sensing , 27 ( 5 ): 568 - 577 [ DOI: 10.1080/07038992.2001.10854896 http://dx.doi.org/10.1080/07038992.2001.10854896 ]

Xing X W . 2014 . Research on Key Technologies for Ship Surveillance based on HRWS SAR Imagery . Changsha : National University of Defense Technology

邢相薇 . 2014 . HRWS SAR图像舰船目标监视关键技术研究 . 长沙 : 国防科学技术大学) [ DOI: 10.7666/d.D675358 http://dx.doi.org/10.7666/d.D675358 ]

Zhao M B , He J and Fu Q . 2012 . Survey on fast CFAR detection algorithms for SAR image targets . Acta Automatica Sinica , 38 ( 12 ): 1885 - 1885

赵明波 , 何峻 , 付强 . 2012 . SAR图像CFAR检测的快速算法综述 . 自动化学报 , 38 ( 12 ): 1885- 1895 [ DOI: 10.3724/SP.J.1004.2012.01885 http://dx.doi.org/10.3724/SP.J.1004.2012.01885 ]

Zhao Z . 2013 . Research on the Key Technologies for Ship Surveillance by Integration of Space-Borne SAR and AIS Changsha: National University of Defense Technology 赵志 . 2013. 基于星载SAR与AIS综合的舰船目标监视关键技术研究 . 长沙 : 国防科学技术大学) [ DOI: 10.7666/d.D676110 http://dx.doi.org/10.7666/d.D676110 ]

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