首页|联合小波阈值和F-NLM去噪的高分辨率SAR舰船检测方法

联合小波阈值和F-NLM去噪的高分辨率SAR舰船检测方法

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
[目的]针对高分辨率合成孔径雷达(SAR)舰船目标多场景、多尺度、密集排布的显著特征,以及成像过程中相干噪声导致目标边缘细节模糊的问题,提出一种融合小波阈值和快速非局部均值滤波(F-NLM)去噪的高分辨率SAR舰船检测方法.[方法]首先,利用小波阈值与F-NLM融合去噪模块预处理SAR图像,来降低海杂波噪声及增强检测目标细节特征和边缘信息,使提取的特征更具判别性.然后,选用YOLOv7 检测算法结合双向特征金字塔网络来对多尺度特征有效聚合,以进一步提高模型准确率.[结果]实验结果显示,使用去噪数据集D-SSDD得到的检测平均准确度可达 98.69%,虚警率降低至 2.37%.[结论]研究表明,所提方法不仅能均匀背景杂波以提高图像质量,还能提高多尺度特征信息的交互性,保证目标检测精度和准确度.
Method of joint wavelet thresholding and F-NLM de-noising for high-resolution SAR ship detection
[Objective]Aiming at the significant features of high-resolution synthetic aperture radar(SAR)ship targets with multiple scenes,multi-scale and dense arrangements,and the problem of the blurring of tar-get edge details due to coherent noise in the imaging process,a high-resolution SAR ship detection method is proposed with joint wavelet thresholding and fast non-local mean(F-NLM)de-noising.[Methods]First,wavelet thresholding and F-NLM de-noising modules are utilized to preprocess the SAR image and reduce the sea clutter noise,enhance the detailed features and edge information of the detection target,and make the ex-tracted features more discriminative.Next,a YOLOv7 detection algorithm combined with a bi-directional fea-ture pyramid network(Bi-FPN)is selected to effectively aggregate the multi-scale features and further im-prove the model's accuracy.[Results]The experimental results show that the average precision of ship de-tection using the de-noised dataset D-SSDD can reach 98.69%and the false alarm rate is reduced to 2.37%.[Conclusions]It is clear that the proposed high-resolution SAR ship detection method not only homogen-izes the background clutter to improve the image quality,but also improves the interactivity of multi-scale fea-ture information to ensure precise and accurate target detection.

radar target recognitionimage processingSAR ship detectionwavelet transformswavelet thresholdfast non-local mean(F-NLM)bi-directional feature pyramid network(Bi-FPN)YOLOv7

童亮、刘丹、彭中波、邹涵、王露萌、张春玉

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重庆交通大学 航运与船舶工程学院,重庆 400074

雷达目标识别 图像处理 SAR舰船检测 小波变换 小波阈值 快速非局部均值滤波 双向特征金字塔网络(Bi-FPN) YOLOv7

2024

10.19693/j.issn.1673-3185.03477

中国舰船研究
中国舰船研究设计中心

中国舰船研究

CSTPCD北大核心
影响因子：0.496
ISSN：1673-3185
年,卷(期)：2024.19(6)