{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:16:23Z","timestamp":1775088983919,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,8]],"date-time":"2023-04-08T00:00:00Z","timestamp":1680912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61975043"],"award-info":[{"award-number":["61975043"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62101160"],"award-info":[{"award-number":["62101160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the increasing demand for the wide-area refined detection of aircraft targets, remote sensing cameras have adopted an ultra-large area-array detector as a new imaging mode to obtain broad width remote sensing images (RSIs) with higher resolution. However, this imaging technology introduces new special image degradation characteristics, especially the weak target energy and the low signal-to-noise ratio (SNR) of the image, which seriously affect the target detection capability. To address the aforementioned issues, we propose an aircraft detection method for RSIs with low SNR, termed L-SNR-YOLO. In particular, the backbone is built blending a swin-transformer and convolutional neural network (CNN), which obtains multiscale global and local RSI information to enhance the algorithm\u2019s robustness. Moreover, we design an effective feature enhancement (EFE) block integrating the concept of nonlocal means filtering to make the aircraft features significant. In addition, we utilize a novel loss function to optimize the detection accuracy. The experimental results demonstrate that our L-SNR-YOLO achieves better detection performance in RSIs than several existing advanced methods.<\/jats:p>","DOI":"10.3390\/rs15081971","type":"journal-article","created":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T03:19:54Z","timestamp":1681096794000},"page":"1971","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Aircraft Target Detection in Low Signal-to-Noise Ratio Visible Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Ruize","family":"Niu","sequence":"first","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5504-8480","authenticated-orcid":false,"given":"Xiyang","family":"Zhi","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Shikai","family":"Jiang","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Jinnan","family":"Gong","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7202-2127","authenticated-orcid":false,"given":"Lijian","family":"Yu","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kohley, R., Gare, P., Vetel, C., Marchais, D., and Chassat, F. 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