{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T19:47:38Z","timestamp":1768420058293,"version":"3.49.0"},"reference-count":50,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T00:00:00Z","timestamp":1622764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB0503600"],"award-info":[{"award-number":["2017YFB0503600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004613","name":"China Geological Survey","doi-asserted-by":"publisher","award":["DD20191006"],"award-info":[{"award-number":["DD20191006"]}],"id":[{"id":"10.13039\/501100004613","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41872253"],"award-info":[{"award-number":["41872253"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aircraft is a means of transportation and weaponry, which is crucial for civil and military fields to detect from remote sensing images. However, detecting aircraft effectively is still a problem due to the diversity of the pose, size, and position of the aircraft and the variety of objects in the image. At present, the target detection methods based on convolutional neural networks (CNNs) lack the sufficient extraction of remote sensing image information and the post-processing of detection results, which results in a high missed detection rate and false alarm rate when facing complex and dense targets. Aiming at the above questions, we proposed a target detection model based on Faster R-CNN, which combines multi-angle features driven and majority voting strategy. Specifically, we designed a multi-angle transformation module to transform the input image to realize the multi-angle feature extraction of the targets in the image. In addition, we added a majority voting mechanism at the end of the model to deal with the results of the multi-angle feature extraction. The average precision (AP) of this method reaches 94.82% and 95.25% on the public and private datasets, respectively, which are 6.81% and 8.98% higher than that of the Faster R-CNN. The experimental results show that the method can detect aircraft effectively, obtaining better performance than mature target detection networks.<\/jats:p>","DOI":"10.3390\/rs13112207","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T01:56:40Z","timestamp":1623031000000},"page":"2207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN"],"prefix":"10.3390","volume":"13","author":[{"given":"Fengcheng","family":"Ji","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3422-7399","authenticated-orcid":false,"given":"Dongping","family":"Ming","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"},{"name":"Polytechnic Center for Natural Resources Big-Data, Ministry of Natural Resources of China, Beijing 100036, China"}]},{"given":"Beichen","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"given":"Jiawei","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"given":"Yuanzhao","family":"Qing","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"given":"Tongyao","family":"Du","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"given":"Xinyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"158097","DOI":"10.1109\/ACCESS.2020.3019956","article-title":"Convolutional Neural Network Based Weakly Supervised Learning for Aircraft Detection From Remote Sensing Image","volume":"8","author":"Wu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106132","DOI":"10.1016\/j.asoc.2020.106132","article-title":"A benchmark data set for aircraft type recognition from remote sensing images","volume":"89","author":"Wu","year":"2020","journal-title":"Appl. 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