基于多层显著性模型的SAR图像舰船目标检测

doi:10.12305/j.issn.1001-506X.2024.02.12

系统工程与电子技术 ›› 2024, Vol. 46 ›› Issue (2): 478-487.doi: 10.12305/j.issn.1001-506X.2024.02.12

• 传感器与信号处理 • 上一篇

基于多层显著性模型的SAR图像舰船目标检测

扈琪¹, 胡绍海²^,³^,*, 刘帅奇¹

1. 河北大学电子信息工程学院, 河北保定 071002
2. 北京交通大学信息科学研究所, 北京 100044
3. 现代信息科学与网络技术北京市重点实验室, 北京 100044

收稿日期:2022-12-14 出版日期:2024-01-25 发布日期:2024-02-06
通讯作者: 胡绍海
作者简介:扈琪(1994—), 女, 博士研究生, 主要研究方向为SAR图像目标检测、计算机视觉
胡绍海(1964—), 男, 教授, 博士, 主要研究方向为图像融合、信号处理
刘帅奇(1986—), 男, 教授, 博士, 主要研究方向为图像处理、信号处理
基金资助:
国家自然科学基金(62172030);国家自然科学基金(62172139);河北省自然科学基金(F2022201055);中国博士后科学基金(2022M713361);河北省高等学校科学技术研究项目(BJ2020030);模式识别国家重点实验室开放课题基金(202200007)

Ship detection in SAR image based on multi-layer saliency model

Qi HU¹, Shaohai HU²^,³^,*, Shuaiqi LIU¹

1. College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
2. Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
3. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China

Received:2022-12-14 Online:2024-01-25 Published:2024-02-06
Contact: Shaohai HU

摘要/Abstract

摘要：

针对合成孔径雷达图像舰船目标检测问题, 提出了一种结合选择机制与轮廓信息的多层显著性目标检测方法。首先, 利用非下采样剪切波和频谱残差法进行全局显著性区域提取。其次, 提出了一种基于动态恒虚警率的活动轮廓显著性模型, 逐步滤除候选区域的虚警, 提取目标轮廓, 从而实现目标的精确检测。所提方法能够由粗到细地快速捕获目标区域, 从而实现高效、高分辨率合成孔径雷达图像舰船检测。最后, 在真实SAR数据集进行了测试, 与其他经典的舰船检测方法相比, 所提算法不仅有效地抑制了海杂波的影响, 而且在检测精度上有较大提高。

关键词: SAR图像目标检测, 非下采样剪切波变换, 显著性检测, 活动轮廓模型

Abstract:

Aiming at the problem of ship detection in synthetic aperture radar (SAR) images, a multi-layer saliency target detection method combining selection mechanism and contour information is proposed. Firstly, non-subsampled shearlet transform (NSST) and spectral residual method are used to extract the globally significant region. Secondly, an active contour saliency model based on dynamic constant false alarm rate (CFAR) is proposed to filter out the false alarms of candidate regions step by step and extract the target contour, so as to realize the accurate detection of targets. The proposed method can quickly capture the target area from coarse to fine, so as to achieve high-efficiency and high-resolution SAR image ship detection. Finally, the algorithm is tested on real SAR datasets. Compared with other classical ship detection methods, the proposed algorithm not only effectively suppresses the influence of sea clutter, but also greatly improves the detection accuracy.

Key words: synthetic aperture radar (SAR) image target detection, non-subsampled shearlet transform (NSST), saliency detection, active contour model

中图分类号:

TN957.52

扈琪, 胡绍海, 刘帅奇. 基于多层显著性模型的SAR图像舰船目标检测[J]. 系统工程与电子技术, 2024, 46(2): 478-487.

Qi HU, Shaohai HU, Shuaiqi LIU. Ship detection in SAR image based on multi-layer saliency model[J]. Systems Engineering and Electronics, 2024, 46(2): 478-487.

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图像	指标	LNM- CFAR	SCE- CFAR	VAB	SP- CFAR	SD	本文算法
Ⅰ	P_d	0.950	1.000	0.993	0.955	0.999	0.995
Ⅰ	T_loss	0.053	0.006	0.008	0.047	0.010	0.005
Ⅱ	P_d	0.922	0.982	0.651	0.733	0.963	0.980
Ⅱ	T_loss	0.080	0.023	0.349	0.268	0.049	0.021
Ⅲ	P_d	0.440	0.577	0.730	0.395	0.693	0.941
Ⅲ	T_loss	0.562	0.425	0.271	0.605	0.309	0.060
Ⅳ	P_d	0.580	0.543	0.487	0.271	0.584	0.735
Ⅳ	T_loss	0.421	0.458	0.518	0.730	0.430	0.269

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基于多层显著性模型的SAR图像舰船目标检测

Ship detection in SAR image based on multi-layer saliency model

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