基于S-HOG的遥感图像舰船目标检测

doi:10.11896/jsjkx.191200090

摘要/Abstract

摘要： 随着高分辨率卫星遥感成像技术的不断发展,可见光遥感图像舰船目标检测成为热门课题,其在军舰探测、精确制导等军用领域以及海面搜救、渔船监测等民用领域具有极其重要的战略意义。针对遥感图像中的舰船检测易受云雾、波浪、岛屿等因素干扰导致虚警率高的问题,提出了基于舰船方向梯度直方图(Ship Histagram of Oriented Gradient,S-HOG)特征的舰船鉴别算法。首先利用异常点检测提取目标候选区域得到可疑目标切片,然后统计其S-HOG特征剔除虚警,从而有效提取真正的舰船目标。实验结果表明,所提算法能在保证高检测率的同时显著降低虚警率,抗干扰能力强,鲁棒性高。

关键词: 感兴趣区域, 舰船方向梯度直方图, 舰船检测, 遥感图像, 异常点检测

Abstract: With the continuous development of high-resolution satellite remote sensing imaging technology,ship target detection based on visible remote sensing image has become a hot topic,which is of great strategic significance in military fields such as warship detection,precise guidance,and civilian fields such as sea search and rescue,fishing vessel monitoring,etc.Aiming at the problem that ship detection in remote sensing image is easy to be interfered by cloud,wave,island and other factors,which leads to high false alarm rate,a ship identification algorithm based on the characteristics of ship histogram of oriented gradient (S-HOG) is proposed.Firstly,the candidate region of the target is extracted by abnormal point detection to get the suspicious target slice,and then the S-HOG feature is counted to eliminate the false alarm,so as to effectively extract the real ship target.Experimental results show that the algorithm can significantly reduce the false alarm rate while ensuring high detection rate,and has strong anti-interference ability and high robustness.

Key words: Abnormal point detection, Remote sensing image, Ship detection, Ship histogram of oriented gradient, Target candidate region

中图分类号:

TP751

丁荣莉, 李杰, 张曼, 刘艳丽, 伍伟. 基于S-HOG的遥感图像舰船目标检测[J]. 计算机科学, 2020, 47(11A): 248-252. https://doi.org/10.11896/jsjkx.191200090

DING Rong-li, LI Jie, ZHANG Man, LIU Yan-li, WU Wei. Ship Target Detection in Remote Sensing Image Based on S-HOG[J]. Computer Science, 2020, 47(11A): 248-252. https://doi.org/10.11896/jsjkx.191200090

参考文献

[1] CHEN L,WANG Z R,HAN Z,et al.Ship target detection and recognition method based on visible light remote sensing image [J].Science & Technology Review,2017,35(20):77-85.
[2] LIN Y D.Research on Object Detection Algorithm of OpticalRemote Sensing Image under Complex Background [D].Southwest Jiaotong University,2017.
[3] WANG Y Q,MA L,TIAN Y.Overview of Ship Target Detection and Recognition in Optical Remote Sensing Images [J].Journal of Automation,2011,37(9):1029-1039.
[4] TONG S,KANG S,WU X,et al.A ship target automatic detec-tion method for high-resolution remote sensing[C]//IGARSS-2016.2016:1258-1261.
[5] YANG G,LI B,JI S,et al.Ship detection from optical satellite images based on sea surface analysis[J].IEEE Geoscience & Remote SensingLetters,2013,11(3):641-645.
[6] SHI P.Ship target automatic detection technology based on optical remote sensing image [D].University of Science and Technology of China,2010.
[7] AN Y,WANG X F,XIA X Z,et al.Ship target detection in optical remote sensing image of naval battlefield [J].Wuhan University Journal (Engineering Science),2015,48(4):568-573.
[8] ZHAO C H,LUAN S J.Phase detection of ship target based on optical remote sensing image [J].Journal of Shenyang University (Natural Science Edition),2015,27(5):369-375.
[9] GUO C L,MA Q,ZHANG L M.Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform [C]//Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition.Anchorage,AL,USA,2008:23-28.
[10] XU F.Research on key technologies for automatic detection of sea surface targets in visible light remote sensing images[D].University of Chinese Academy of Sciences (Changchun Institute of Optics,Fine Mechaincs and Physics,Chinees Academy of Sciences),2018.
[11] JI X F,QIN N L.Design and implementation of multi-target detection and recognition algorithm for optical remote sensing ima-ge [J].Journal of Computer Applications,2015,35(11):3302-3307.
[12] ZHU C R,ZHOU H,WANG R S,et al.A novel hierarchicalmethod of ship detection from spaceborne optical image based on shape and texture features [J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(9):3446-3456.
[13] QI S,MA J,LIN J,et al.Unsupervised ship detection based on saliency and S-HOG descriptor from optical satellite images[J].IEEE Geo-science & Remote Sensing Letters,2015,12(7):1451-1455.
[14] YIN Y,HUANG H,ZHANG Z X.Research on Ship Target Detection Technology Based on Optical Remote Sensing Images [J].Computer Science,46(3):88-93.
[15] PENG J T.Research on ship target detection technology of optical remote sensing image [D].Shanghai:Donghua University,2017.
[16] GAO L N,BI F K,LONG T,et al.A sea surface ship detection algorithm for optical remote sensing images [J].Journal of Tsi-nghua University(Science and Technology),2011,51(1):105-110.

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