{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T08:33:02Z","timestamp":1773909182514,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T00:00:00Z","timestamp":1666224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nondestructive Detection and Monitoring Technology for High Speed Transportation Facilities","award":["NJ2020014"],"award-info":[{"award-number":["NJ2020014"]}]},{"name":"Nondestructive Detection and Monitoring Technology for High Speed Transportation Facilities","award":["buctrc202221"],"award-info":[{"award-number":["buctrc202221"]}]},{"name":"Key Laboratory of Ministry of Industry and Information Technology","award":["NJ2020014"],"award-info":[{"award-number":["NJ2020014"]}]},{"name":"Key Laboratory of Ministry of Industry and Information Technology","award":["buctrc202221"],"award-info":[{"award-number":["buctrc202221"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["NJ2020014"],"award-info":[{"award-number":["NJ2020014"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["buctrc202221"],"award-info":[{"award-number":["buctrc202221"]}]},{"name":"Fund of Fundamental Research Funds for the Central Universities","award":["NJ2020014"],"award-info":[{"award-number":["NJ2020014"]}]},{"name":"Fund of Fundamental Research Funds for the Central Universities","award":["buctrc202221"],"award-info":[{"award-number":["buctrc202221"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs14205257","type":"journal-article","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:34:30Z","timestamp":1666312470000},"page":"5257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Filtered Convolution for Synthetic Aperture Radar Images Ship Detection"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4310-7786","authenticated-orcid":false,"given":"Luyang","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China"},{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haitao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhong","family":"Pan","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunlei","family":"Huo","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, G., Kang, H., Wang, Q., Tian, Y., and Wan, B. 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