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

Abstract

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

CLC Number:

TN957.52

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.

Figures/Tables 11

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

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