{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T13:58:01Z","timestamp":1762869481798,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T00:00:00Z","timestamp":1734393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Heilongjiang Postdoctoral Fund","award":["LBH-Z23016"],"award-info":[{"award-number":["LBH-Z23016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has become an important factor restricting the effectiveness and reliability of aircraft detection algorithms. Although a large amount of research has been devoted to breaking through few-sample-driven aircraft detection technology, most algorithms still struggle to effectively solve the problems of missed target detection and false alarms caused by numerous environmental interferences in bird-eye optical remote sensing scenes. To further aircraft detection research, we have established a new dataset, Aircraft Detection in Complex Optical Scene (ADCOS), sourced from various platforms including Google Earth, Microsoft Map, Worldview-3, Pleiades, Ikonos, Orbview-3, and Jilin-1 satellites. It integrates 3903 meticulously chosen images of over 400 famous airports worldwide, containing 33,831 annotated instances employing the oriented bounding box (OBB) format. Notably, this dataset encompasses a wide range of various targets characteristics including multi-scale, multi-direction, multi-type, multi-state, and dense arrangement, along with complex relationships between targets and backgrounds like cluttered backgrounds, low contrast, shadows, and occlusion interference conditions. Furthermore, we evaluated nine representative detection algorithms on the ADCOS dataset, establishing a performance benchmark for subsequent algorithm optimization. The latest dataset will soon be available on the Github website.<\/jats:p>","DOI":"10.3390\/rs16244699","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T03:46:02Z","timestamp":1734407162000},"page":"4699","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing Imagery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4418-605X","authenticated-orcid":false,"given":"Jianming","family":"Hu","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":"Bingxian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Space Mechanics and Electricity, Beijing 100076, China"}]},{"given":"Tianjun","family":"Shi","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Qi","family":"Cui","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8909-9857","authenticated-orcid":false,"given":"Xiaogang","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5553","DOI":"10.1109\/TGRS.2016.2569141","article-title":"Weakly supervised learning based on coupled convolutional neural networks for aircraft detection","volume":"54","author":"Zhang","year":"2016","journal-title":"IEEE Trans. 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