Ship Detection Transformer in SAR Images Based on Key Scattering Points Feature Aggregation and Context Feature Refinement
In recent years, deep learning algorithms have demonstrated significant advancements in the field of ship detection using synthetic aperture radar (SAR). Nevertheless, two primary challenges persist in the task of SAR ship detection: first, owing to the unique imaging mechanism, targets in SAR image...
Saved in:
| Published in | IEEE journal of selected topics in applied earth observations and remote sensing Vol. 18; pp. 17820 - 17836 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
2025
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | In recent years, deep learning algorithms have demonstrated significant advancements in the field of ship detection using synthetic aperture radar (SAR). Nevertheless, two primary challenges persist in the task of SAR ship detection: first, owing to the unique imaging mechanism, targets in SAR images are typically represented by scattering points, posing challenges for accurate feature extraction and leading to issues of inaccurate localization. Second, the detectors are susceptible to generating false alarms due to interference from the complex backgrounds of inshore scenes. In order to mitigate the issues mentioned above, a ship detection transformer based on key scattering points feature aggregation and context feature refinement is proposed. Specifically, considering that the ship targets exist in the form of scattering points in the SAR images, a key scattering points feature aggregation module is designed to mine and aggregate the key scattering points feature of ship targets. By this method, it is beneficial to generate more accurate feature representation for improving the localization performance of the detectors. Furthermore, to address the issue of excessive false alarms under complex background interference, a context feature refinement module is designed to augment the semantic representation and context information of feature maps. Extensive experiments are conducted on the two public datasets to substantiate the superiority of our proposed detector compared with other state-of-the-art methods. |
|---|---|
| AbstractList | In recent years, deep learning algorithms have demonstrated significant advancements in the field of ship detection using synthetic aperture radar (SAR). Nevertheless, two primary challenges persist in the task of SAR ship detection: first, owing to the unique imaging mechanism, targets in SAR images are typically represented by scattering points, posing challenges for accurate feature extraction and leading to issues of inaccurate localization. Second, the detectors are susceptible to generating false alarms due to interference from the complex backgrounds of inshore scenes. In order to mitigate the issues mentioned above, a ship detection transformer based on key scattering points feature aggregation and context feature refinement is proposed. Specifically, considering that the ship targets exist in the form of scattering points in the SAR images, a key scattering points feature aggregation module is designed to mine and aggregate the key scattering points feature of ship targets. By this method, it is beneficial to generate more accurate feature representation for improving the localization performance of the detectors. Furthermore, to address the issue of excessive false alarms under complex background interference, a context feature refinement module is designed to augment the semantic representation and context information of feature maps. Extensive experiments are conducted on the two public datasets to substantiate the superiority of our proposed detector compared with other state-of-the-art methods. |
| Author | Li, Wei Meng, Fanyu Yang, Zhu Shi, Hao Yin, Yifei |
| Author_xml | – sequence: 1 givenname: Yifei orcidid: 0009-0002-3281-5557 surname: Yin fullname: Yin, Yifei organization: School of Information and Electronics, Beijing Institute of Technology, Beijing, China – sequence: 2 givenname: Zhu orcidid: 0000-0001-9640-7443 surname: Yang fullname: Yang, Zhu organization: School of Information and Electronics, Beijing Institute of Technology, Beijing, China – sequence: 3 givenname: Hao orcidid: 0000-0002-2013-6592 surname: Shi fullname: Shi, Hao email: shihao@bit.edu.cn organization: School of Information and Electronics, Beijing Institute of Technology, Beijing, China – sequence: 4 givenname: Fanyu orcidid: 0009-0005-3606-3745 surname: Meng fullname: Meng, Fanyu organization: School of Information and Electronics, Beijing Institute of Technology, Beijing, China – sequence: 5 givenname: Wei orcidid: 0000-0001-7015-7335 surname: Li fullname: Li, Wei organization: School of Information and Electronics, Beijing Institute of Technology, Beijing, China |
| BookMark | eNpFkU1v1DAQQC1UJLaFXwAH_4Fs7fgjznFZKCxUAm2WszWxx8FV16lsI1F-fdNuVU4jzcx7l3dOztKckJD3nK05Z_3lt-Gw2Q_rlrVqLZRhnexekVXLFW-4EuqMrHgv-oZLJt-Q81JuGNNt14sV-Tf8jnf0E1Z0Nc6JHjKkEuZ8xExjosNmT3dHmLDQj1DQ0-XlO97TwUGtmGOa6M85plroFUL9k5FupinjBE8ySJ5u51Txb3257zHEhEdM9S15HeC24LvneUF-XX0-bL821z--7Lab68a10nSN6xQXIHUwCoQchZNBCKbl2DIPPrSSiQChd1J7PRoOJnjkRhg_uhH8wlyQ3cnrZ7ixdzkeId_bGaJ9Wsx5spBrdLdoldeuHzkzThupuBwZE3zU7eLywmi2uMTJ5fJcSsbw4uPMPqawpxT2MYV9TrFQH05URMT_BGei14aLB7rniLE |
| CODEN | IJSTHZ |
| Cites_doi | 10.1109/JSTARS.2017.2692820 10.1109/ACCESS.2020.3005861 10.1109/CVPR52688.2022.01325 10.1109/TGRS.2021.3128060 10.3390/rs14081908 10.1109/LGRS.2024.3461212 10.1007/978-3-030-01264-9_45 10.1109/ICCV48922.2021.00360 10.1109/TAP.1986.1143771 10.3390/rs14205247 10.1109/LGRS.2016.2618604 10.3390/rs13183690 10.1109/EURAD.2005.1605556 10.1109/TGRS.2024.3515150 10.1109/ICCV.2019.00667 10.1109/CVPR52729.2023.00721 10.1109/TGRS.2020.3005151 10.1109/MSPEC.1967.5217220 10.1016/j.cpc.2007.11.005 10.1109/TGRS.2022.3141125 10.1109/CVPR.2017.690 10.1109/JSTARS.2017.2755672 10.1109/JSTARS.2024.3435989 10.1007/s11760-018-1408-4 10.1109/CVPR52729.2023.00297 10.1109/JSTARS.2025.3533140 10.1109/TGRS.2024.3454308 10.1109/TGRS.2024.3419893 10.1109/TGRS.2019.2899337 10.1109/ICCV.2019.00972 10.1109/TGRS.2022.3141407 10.1109/TGRS.2021.3130117 10.1016/j.patcog.2020.107787 10.1109/JSTARS.2024.3461723 10.3390/rs14061488 10.12000/JR22007 10.1109/CVPR.2019.00091 10.1109/TGRS.2021.3095386 10.1109/TGRS.2020.2997200 10.1109/CVPR.2009.5206848 10.1109/ICCV.2015.169 10.1109/7.135446 10.3390/rs11070765 10.1109/TGRS.2021.3108585 10.1109/TGRS.2020.2987907 10.1109/JSTARS.2024.3514898 10.1109/TGRS.2024.3394405 10.1109/CVPR.2016.90 10.1109/TGRS.2021.3130899 10.1109/CVPR.2014.81 10.1109/ICCV.2017.322 10.1007/s11432-017-9405-6 10.1109/ICCV48922.2021.00363 10.1109/TGRS.2024.3451399 10.1609/aaai.v36i3.20158 10.1109/TGRS.2023.3267495 10.1109/TGRS.2022.3202495 10.1007/978-3-030-58452-8_13 10.1109/cvpr42600.2020.00978 10.l007/978-3-319-46448-0_2 10.1109/ICCV.2017.324 10.1109/ICCSIT.2010.5563693 10.3390/rs9080860 10.3390/rs14092238 10.1109/CVPR.2018.00745 10.1016/j.isprsjprs.2024.12.011 10.1109/JSTARS.2024.3524402 10.1109/tpami.2016.2577031 10.1109/TGRS.2022.3146027 10.1109/CVPR.2016.91 10.1109/JSTARS.2024.3358058 10.1109/CVPR52729.2023.01780 10.1109/TGRS.2023.3249349 10.1109/LGRS.2022.3192559 |
| ContentType | Journal Article |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION DOA |
| DOI | 10.1109/JSTARS.2025.3580747 |
| DatabaseName | IEEE Xplore (IEEE) IEEE Xplore Open Access (Activated by CARLI) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geology |
| EISSN | 2151-1535 |
| EndPage | 17836 |
| ExternalDocumentID | oai_doaj_org_article_5d6c9b108c684514b0031b62dbcd3860 10_1109_JSTARS_2025_3580747 11039681 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Key Research and Development Program of China grantid: 2021YFA0715204 |
| GroupedDBID | 0R~ 29I 4.4 5GY 5VS 6IK 97E AAFWJ AAJGR AASAJ AAWTH ABAZT ABVLG ACIWK AENEX AETIX AFPKN AFRAH AGSQL ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ DU5 EBS EJD ESBDL GROUPED_DOAJ HZ~ IFIPE IPLJI JAVBF M43 O9- OCL OK1 RIA RIE RNS AAYXX CITATION RIG |
| ID | FETCH-LOGICAL-c2487-c7513a46f85a34b3c4f33064b20dadf2403faf9c46d6b81a8fde1838dbcbad5a3 |
| IEDL.DBID | DOA |
| ISSN | 1939-1404 |
| IngestDate | Wed Aug 27 01:09:43 EDT 2025 Thu Jul 31 00:28:00 EDT 2025 Wed Aug 27 02:13:11 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0/legalcode |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c2487-c7513a46f85a34b3c4f33064b20dadf2403faf9c46d6b81a8fde1838dbcbad5a3 |
| ORCID | 0000-0001-9640-7443 0009-0002-3281-5557 0000-0002-2013-6592 0000-0001-7015-7335 0009-0005-3606-3745 |
| OpenAccessLink | https://doaj.org/article/5d6c9b108c684514b0031b62dbcd3860 |
| PageCount | 17 |
| ParticipantIDs | crossref_primary_10_1109_JSTARS_2025_3580747 ieee_primary_11039681 doaj_primary_oai_doaj_org_article_5d6c9b108c684514b0031b62dbcd3860 |
| PublicationCentury | 2000 |
| PublicationDate | 20250000 2025-00-00 2025-01-01 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – year: 2025 text: 20250000 |
| PublicationDecade | 2020 |
| PublicationTitle | IEEE journal of selected topics in applied earth observations and remote sensing |
| PublicationTitleAbbrev | JSTARS |
| PublicationYear | 2025 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| References | ref13 ref57 ref12 ref56 ref15 ref59 ref14 ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 Zhang (ref45) 2023 ref51 ref50 Jocher (ref34) 2024 Chen (ref42) 2024 ref46 Jocher (ref33) 2023 ref48 ref47 ref41 Loshchilov (ref80) 2019 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref82 ref81 ref40 ref79 ref78 ref37 ref31 ref75 ref30 ref74 ref77 ref32 ref76 ref2 Zhu (ref44) 2021 ref1 ref39 ref38 Tian (ref35) 2025 Ge (ref36) 2021 ref71 ref70 ref73 ref72 ref24 ref68 ref23 ref67 Kong (ref83) 2019; 29 ref26 ref25 ref69 ref20 ref64 ref63 ref22 ref66 ref21 ref65 ref28 ref27 ref29 ref60 ref62 ref61 |
| References_xml | – ident: ref15 doi: 10.1109/JSTARS.2017.2692820 – ident: ref77 doi: 10.1109/ACCESS.2020.3005861 – ident: ref43 doi: 10.1109/CVPR52688.2022.01325 – ident: ref54 doi: 10.1109/TGRS.2021.3128060 – volume-title: Proc. Int. Conf. Learn. Representations year: 2019 ident: ref80 article-title: Decoupled weight decay regularization – ident: ref53 doi: 10.3390/rs14081908 – ident: ref64 doi: 10.1109/LGRS.2024.3461212 – ident: ref37 doi: 10.1007/978-3-030-01264-9_45 – ident: ref38 doi: 10.1109/ICCV48922.2021.00360 – ident: ref2 doi: 10.1109/TAP.1986.1143771 – ident: ref63 doi: 10.3390/rs14205247 – ident: ref68 doi: 10.1109/LGRS.2016.2618604 – volume-title: Proc. Int. Conf. Learn. Representations year: 2023 ident: ref45 article-title: DINO: DETR with improved denoising anchor boxes for end-to-end object detection – ident: ref76 doi: 10.3390/rs13183690 – ident: ref1 doi: 10.1109/EURAD.2005.1605556 – ident: ref66 doi: 10.1109/TGRS.2024.3515150 – ident: ref22 doi: 10.1109/ICCV.2019.00667 – ident: ref32 doi: 10.1109/CVPR52729.2023.00721 – ident: ref55 doi: 10.1109/TGRS.2020.3005151 – ident: ref73 doi: 10.1109/MSPEC.1967.5217220 – ident: ref72 doi: 10.1016/j.cpc.2007.11.005 – ident: ref3 doi: 10.1109/TGRS.2022.3141125 – year: 2024 ident: ref42 article-title: LW-DETR: A transformer replacement to YOLO for real-time detection – ident: ref23 doi: 10.1109/CVPR.2017.690 – ident: ref48 doi: 10.1109/JSTARS.2017.2755672 – ident: ref60 doi: 10.1109/JSTARS.2024.3435989 – ident: ref14 doi: 10.1007/s11760-018-1408-4 – ident: ref46 doi: 10.1109/CVPR52729.2023.00297 – ident: ref50 doi: 10.1109/JSTARS.2025.3533140 – volume-title: Proc. Int. Conf. Learn. Representations year: 2021 ident: ref44 article-title: Deformable DETR: Deformable transformers for end-to-end object detection – ident: ref12 doi: 10.1109/TGRS.2024.3454308 – ident: ref49 doi: 10.1109/TGRS.2024.3419893 – year: 2025 ident: ref35 article-title: Yolov12: Attention-centric real-time object detectors – ident: ref16 doi: 10.1109/TGRS.2019.2899337 – ident: ref21 doi: 10.1109/ICCV.2019.00972 – ident: ref57 doi: 10.1109/TGRS.2022.3141407 – ident: ref9 doi: 10.1109/TGRS.2021.3130117 – ident: ref27 doi: 10.1016/j.patcog.2020.107787 – ident: ref65 doi: 10.1109/JSTARS.2024.3461723 – year: 2024 ident: ref34 article-title: Ultralytics yolo11 – year: 2023 ident: ref33 article-title: Ultralytics YOLOv8 – ident: ref62 doi: 10.3390/rs14061488 – ident: ref47 doi: 10.12000/JR22007 – ident: ref82 doi: 10.1109/CVPR.2019.00091 – volume: 29 start-page: 7389 year: 2019 ident: ref83 article-title: Foveabox: Beyond anchor-based object detector publication-title: Comput. Vis. Pattern Recognit. – ident: ref7 doi: 10.1109/TGRS.2021.3095386 – ident: ref52 doi: 10.1109/TGRS.2020.2997200 – ident: ref79 doi: 10.1109/CVPR.2009.5206848 – ident: ref20 doi: 10.1109/ICCV.2015.169 – ident: ref13 doi: 10.1109/7.135446 – ident: ref78 doi: 10.3390/rs11070765 – ident: ref4 doi: 10.1109/TGRS.2021.3108585 – ident: ref6 doi: 10.1109/TGRS.2020.2987907 – ident: ref51 doi: 10.1109/JSTARS.2024.3514898 – ident: ref17 doi: 10.1109/TGRS.2024.3394405 – ident: ref74 doi: 10.1109/CVPR.2016.90 – ident: ref5 doi: 10.1109/TGRS.2021.3130899 – ident: ref29 doi: 10.1109/CVPR.2014.81 – year: 2021 ident: ref36 article-title: YOLOX: Exceeding YOLO series in 2021 – ident: ref67 doi: 10.1109/ICCV.2017.322 – ident: ref8 doi: 10.1007/s11432-017-9405-6 – ident: ref40 doi: 10.1109/ICCV48922.2021.00363 – ident: ref58 doi: 10.1109/TGRS.2024.3451399 – ident: ref39 doi: 10.1609/aaai.v36i3.20158 – ident: ref71 doi: 10.1109/TGRS.2023.3267495 – ident: ref56 doi: 10.1109/TGRS.2022.3202495 – ident: ref25 doi: 10.1007/978-3-030-58452-8_13 – ident: ref81 doi: 10.1109/cvpr42600.2020.00978 – ident: ref30 doi: 10.l007/978-3-319-46448-0_2 – ident: ref24 doi: 10.1109/ICCV.2017.324 – ident: ref69 doi: 10.1109/ICCSIT.2010.5563693 – ident: ref26 doi: 10.3390/rs9080860 – ident: ref10 doi: 10.3390/rs14092238 – ident: ref75 doi: 10.1109/CVPR.2018.00745 – ident: ref18 doi: 10.1016/j.isprsjprs.2024.12.011 – ident: ref59 doi: 10.1109/JSTARS.2024.3524402 – ident: ref19 doi: 10.1109/tpami.2016.2577031 – ident: ref28 doi: 10.1109/TGRS.2022.3146027 – ident: ref31 doi: 10.1109/CVPR.2016.91 – ident: ref11 doi: 10.1109/JSTARS.2024.3358058 – ident: ref41 doi: 10.1109/CVPR52729.2023.01780 – ident: ref70 doi: 10.1109/TGRS.2023.3249349 – ident: ref61 doi: 10.1109/LGRS.2022.3192559 |
| SSID | ssj0062793 |
| Score | 2.374329 |
| Snippet | In recent years, deep learning algorithms have demonstrated significant advancements in the field of ship detection using synthetic aperture radar (SAR).... |
| SourceID | doaj crossref ieee |
| SourceType | Open Website Index Database Publisher |
| StartPage | 17820 |
| SubjectTerms | Accuracy Detection transformer Detectors Feature extraction Marine vehicles Radar polarimetry Remote sensing Scattering scattering points Semantics ship detection Synthetic aperture radar synthetic aperture radar (SAR) Transformers |
| SummonAdditionalLinks | – databaseName: IEEE Electronic Library (IEL) dbid: RIE link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LT9wwELboSpV64VGoWF7ygWOzJI7jtY8LlKeKql2QuEV-wqpqFi1ZieXXM-NkaUFC6iWKEkcZ5XPG39iebwjZZyo42y98wi2DQ5BZolzGk77WgSmjJDOY7_zzSpzd8Ivb4rZNVo-5MN77uPnM9_A0ruW7iZ3hVNlBhuuWAhOtP0Hk1iRrLdyuYP2osAuERCWoGdNKDGWpOoA-PhiOIBhkRQ-X_fpYTOWfYSiq9b8prxJHl5MVcrWwq9lU8rs3q03PPr-TbPxvw1fJcssz6aDpGGtkyVdfyefTWMd3vk6eR_fjB3rs67gTq6LXC_7qp3Rc0dFgSM__gKt5pIcwzDkKTS79nI5slOOE4Y7-moyr-pEihZxNPR3cQeB-F2GmunI0ql491a_3hz4An0UzN8jNyY_ro7OkLcOQWAbhTAJQZrnmIshC59zklocc4xbDUqddQEG_oIOyXDhhZKZlcB4chXTGGu3gmW-kU00qv0lo4MoBZRE5kynXuZSY6KpD4cBdp06KLvm-QKV8aNQ2yhilpKpsQCwRxLIFsUsOEbnXpiiVHS_Axy_bP68snLDKZKm0QnKgh9GPGcHAPJdLkXbJBgL2930tVlsfXN8mX9CGZhpmh3Tq6czvAjGpzV7skC9P6N7n priority: 102 providerName: IEEE |
| Title | Ship Detection Transformer in SAR Images Based on Key Scattering Points Feature Aggregation and Context Feature Refinement |
| URI | https://ieeexplore.ieee.org/document/11039681 https://doaj.org/article/5d6c9b108c684514b0031b62dbcd3860 |
| Volume | 18 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV05T8MwFLYQEhIL4iiiHJUHRgKJ47j22ALlEgi1VOoW-YQOBNSmEuXX8-ykUCYWlgyJnVjfc_y-5-N7CB0T4YxuZzaimsDF8SQSJqFRW0pHhBKcKH_e-f6BXQ_p7SgbLaX68nvCKnngCrizzDAtVBJzzTgF7x66oWLEKG1SzkK0Dj5vEUxVYzAj7SC3C-xERF5AptYbSmJxBh2-0x9AZEiyU78G2PaZVZZ8UpDu_5VrJbia3ibaqDki7lRt20IrtthGa1chB-98B30OXsbv-MKWYRdVgZ8W3NNO8LjAg04f37zCMDHFXXBRBkOROzvHAx2kNMFV4ce3cVFOsad_s4nFnWcIup-DibAsDA6KVR_l9_O-dcBF_TRiAw17l0_n11GdQiHSBEKRCMyQpJIyxzOZUpVq6lIfcygSG2mcF-Nz0glNmWGKJ5I7Y-En54Cukgbq7KLV4q2wewg7KgzQDZYSHlOZcu4PqUqXGRhqY8NZE50sQMzfK6WMPEQYscgrzHOPeV5j3kRdD_R3US9zHW6A8fPa-Plfxm-ihjfTz_f8wjbjyf5_vPwArfsGV_Mth2i1nMzsETCQUrVCZ2uFw4JfJ3bVGQ |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3fT9swELYQE9petjGY1o0NP-xxKYnjuPZjgbHyq0JtkXiL_JNViBSVVBr89dw5KRtIk3iJosRRTvmcu-9s32dCvjMVnO0VPuGWwSHILFEu40lP68CUUZIZrHc-HYrBOT-6KC7aYvVYC-O9j4vPfBdP41y-m9kFDpXtZDhvKbDQ-hUE_iJryrWWjlewXtTYBUqiElSNaUWGslTtQC_vj8aQDrKiixN_PdxO5Z9AFPX6n2ywEuPLwTsyXFrWLCu56i5q07X3z0QbX2z6e_K2ZZq033SNdbLiqw9k7Vfcyfdug9yPf09v6L6v41qsik6WDNbP6bSi4_6IHl6Ds7mluxDoHIUmx_6Ojm0U5ISAR89m06q-pUgiF3NP-5eQul9GoKmuHI26V3_qx_sjH4DRopmb5Pzg52RvkLQbMSSWQUKTAJhZrrkIstA5N7nlIcfMxbDUaRdQ0i_ooCwXThiZaRmcB1chnbFGO3jmI1mtZpX_RGjgygFpETmTKde5lFjqqkPhwGGnTooO-bFEpbxp9DbKmKekqmxALBHEsgWxQ3YRucemKJYdL8DHL9t_ryycsMpkqbRCciCI0ZMZwcA8l0uRdsgmAvb3fS1Wn_9zfZu8HkxOT8qTw-HxF_IG7WkGZbbIaj1f-K9AU2rzLXbOBxn54jA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Ship+Detection+Transformer+in+SAR+Images+Based+on+Key+Scattering+Points+Feature+Aggregation+and+Context+Feature+Refinement&rft.jtitle=IEEE+journal+of+selected+topics+in+applied+earth+observations+and+remote+sensing&rft.au=Yin%2C+Yifei&rft.au=Yang%2C+Zhu&rft.au=Shi%2C+Hao&rft.au=Meng%2C+Fanyu&rft.date=2025&rft.pub=IEEE&rft.issn=1939-1404&rft.volume=18&rft.spage=17820&rft.epage=17836&rft_id=info:doi/10.1109%2FJSTARS.2025.3580747&rft.externalDocID=11039681 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1939-1404&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1939-1404&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1939-1404&client=summon |