{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:14:41Z","timestamp":1773774881066,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T00:00:00Z","timestamp":1720137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Fund of Science and Technology on Electromagnetic Scattering Key Laboratory","award":["61424090112"],"award-info":[{"award-number":["61424090112"]}]},{"name":"Open Fund of Science and Technology on Electromagnetic Scattering Key Laboratory","award":["2023r019"],"award-info":[{"award-number":["2023r019"]}]},{"name":"Open Fund of Science and Technology on Electromagnetic Scattering Key Laboratory","award":["202410300120Y"],"award-info":[{"award-number":["202410300120Y"]}]},{"DOI":"10.13039\/501100008045","name":"the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology","doi-asserted-by":"publisher","award":["61424090112"],"award-info":[{"award-number":["61424090112"]}],"id":[{"id":"10.13039\/501100008045","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008045","name":"the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology","doi-asserted-by":"publisher","award":["2023r019"],"award-info":[{"award-number":["2023r019"]}],"id":[{"id":"10.13039\/501100008045","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008045","name":"the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology","doi-asserted-by":"publisher","award":["202410300120Y"],"award-info":[{"award-number":["202410300120Y"]}],"id":[{"id":"10.13039\/501100008045","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the NUIST Students\u2019 Platform for Innovation and Entrepreneurship Training Program","award":["61424090112"],"award-info":[{"award-number":["61424090112"]}]},{"name":"the NUIST Students\u2019 Platform for Innovation and Entrepreneurship Training Program","award":["2023r019"],"award-info":[{"award-number":["2023r019"]}]},{"name":"the NUIST Students\u2019 Platform for Innovation and Entrepreneurship Training Program","award":["202410300120Y"],"award-info":[{"award-number":["202410300120Y"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Detecting aircraft targets in Synthetic Aperture Radar (SAR) images is critical for military and civilian applications. However, due to SAR\u2019s special imaging mechanism, aircraft targets often consist of scattering points with large fluctuations in intensity. This often leads to the detector failing to detect weak scattering points. Not only that, previous SAR image aircraft-object-detection models have focused more on detecting and locating targets, with little emphasis on target recognition. This paper proposes a scattering-point-intensity-adaptive detection and recognition network (SADRN). In order to correctly detect the target area, we propose a Self-adaptive Bell-shaped Kernel (SBK) within the detector, which constructs a bell-shaped two-dimensional distribution centered on the target center, making the detection \u201cthreshold\u201d for the target decrease from the center towards the periphery, reducing the missed alarms of weak scattering points at the edges of the target. To help the model adapt to multi-scale targets, we propose the FADLA-34 backbone network, aggregating information from feature maps across different scales. We also embed CBAM into the detector, which enhances the attention to the target area in the spatial dimension and strengthens the extraction of useful features in the channel dimension, reducing interference from the complex background clutter on object detection. Furthermore, to integrate detection and recognition, we introduce the multi-task head, which utilizes the three feature maps from the backbone network to generate the detection boxes and categories of the targets. Finally, the SADRN achieves superior detection and recognition performance on the SAR-AIRcraft-1.0, exceeding other mainstream methods. Visualization and analysis further confirm the effectiveness and superiority of the SADRN.<\/jats:p>","DOI":"10.3390\/rs16132471","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T12:30:59Z","timestamp":1720182659000},"page":"2471","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Integrated Multi-Scale Aircraft Detection and Recognition with Scattering Point Intensity Adaptiveness in Complex Background Clutter SAR Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Xuyuan","family":"Ye","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Chuan","family":"Du","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1109\/JSTARS.2017.2787728","article-title":"Multiple mode SAR raw data simulation and parallel acceleration for Gaofen-3 mission","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. 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