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Filtered Convolution for Synthetic Aperture Radar Images Ship Detection

文献类型：期刊论文

作者Zhang, Luyang2,3; Wang, Haitao3; Wang, Lingfeng1; Pan, Chunhong2; Huo, Chunlei2; Liu, Qiang3; Wang, Xinyao3
刊名REMOTE SENSING
出版日期2022-10-01
卷号14期号:20页码:19
关键词synthetic aperture radar (SAR) remote sensing image ship detection filter convolution coherent speckle noise local weight
DOI10.3390/rs14205257
通讯作者Wang, Haitao(htwang@nuaa.edu.cn)
英文摘要Synthetic aperture radar (SAR) image ship detection is currently a research hotspot in the field of national defense science and technology. However, SAR images contain a large amount of coherent speckle noise, which poses significant challenges in the task of ship detection. To address this issue, we propose filter convolution, a novel design that replaces the traditional convolution layer and suppresses coherent speckle noise while extracting features. Specifically, the convolution kernel of the filter convolution comes from the input and is generated by two modules: the kernel-generation module and local weight generation module. The kernel-generation module is a dynamic structure that generates dynamic convolution kernels using input image or feature information. The local weight generation module is based on the statistical characteristics of the input images or features and is used to generate local weights. The introduction of local weights allows the extracted features to contain more local characteristic information, which is conducive to ship detection in SAR images. In addition, we proved that the fusion of the proposed kernel-generation module and the local weight module can suppress coherent speckle noise in the SAR image. The experimental results show the excellent performance of our method on a large-scale SAR ship detection dataset-v1.0 (LS-SSDD-v1.0). It also achieved state-of-the-art performance on a high-resolution SAR image dataset (HRSID), which confirmed its applicability.
WOS关键词SPECKLE ; NOISE
资助项目Nondestructive Detection and Monitoring Technology for High Speed Transportation Facilities ; Key Laboratory of Ministry of Industry and Information Technology ; Fundamental Research Funds for the Central Universities[NJ2020014] ; Fund of Fundamental Research Funds for the Central Universities[buctrc202221]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者MDPI
WOS记录号WOS:000873506900001
资助机构Nondestructive Detection and Monitoring Technology for High Speed Transportation Facilities ; Key Laboratory of Ministry of Industry and Information Technology ; Fundamental Research Funds for the Central Universities ; Fund of Fundamental Research Funds for the Central Universities
源URL[http://ir.ia.ac.cn/handle/173211/50483]  
专题自动化研究所_模式识别国家重点实验室_遥感图像处理团队
通讯作者Wang, Haitao
作者单位1.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 210016, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Luyang,Wang, Haitao,Wang, Lingfeng,et al. Filtered Convolution for Synthetic Aperture Radar Images Ship Detection[J]. REMOTE SENSING,2022,14(20):19.
APA Zhang, Luyang.,Wang, Haitao.,Wang, Lingfeng.,Pan, Chunhong.,Huo, Chunlei.,...&Wang, Xinyao.(2022).Filtered Convolution for Synthetic Aperture Radar Images Ship Detection.REMOTE SENSING,14(20),19.
MLA Zhang, Luyang,et al."Filtered Convolution for Synthetic Aperture Radar Images Ship Detection".REMOTE SENSING 14.20(2022):19.

入库方式： OAI收割

来源：自动化研究所

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