{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:20:39Z","timestamp":1775665239847,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,9,21]],"date-time":"2018-09-21T00:00:00Z","timestamp":1537488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004750","name":"Aeronautical Science Foundation of China","doi-asserted-by":"publisher","award":["20175896022"],"award-info":[{"award-number":["20175896022"]}],"id":[{"id":"10.13039\/501100004750","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fast and accurate airport detection in remote sensing images is important for many military and civilian applications. However, traditional airport detection methods have low detection rates, high false alarm rates and slow speeds. Due to the power convolutional neural networks in object-detection systems, an end-to-end airport detection method based on convolutional neural networks is proposed in this study. First, based on the common low-level visual features of natural images and airport remote sensing images, region-based convolutional neural networks are chosen to conduct transfer learning for airport images using a limited amount of data. Second, to further improve the detection rate and reduce the false alarm rate, the concepts of \u201cdivide and conquer\u201d and \u201cintegral loss\u2019\u2019 are introduced to establish cascade region proposal networks and multi-threshold detection networks, respectively. Third, hard example mining is used to improve the object discrimination ability and the training efficiency of the network during sample training. Additionally, a cross-optimization strategy is employed to achieve convolution layer sharing between the cascade region proposal networks and the subsequent multi-threshold detection networks, and this approach significantly decreases the detection time. The results show that the method established in this study can accurately detect various types of airports in complex backgrounds with a higher detection rate, lower false alarm rate, and shorter detection time than existing airport detection methods.<\/jats:p>","DOI":"10.3390\/rs10101516","type":"journal-article","created":{"date-parts":[[2018,9,21]],"date-time":"2018-09-21T11:00:25Z","timestamp":1537527625000},"page":"1516","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["End-to-End Airport Detection in Remote Sensing Images Combining Cascade Region Proposal Networks and Multi-Threshold Detection Networks"],"prefix":"10.3390","volume":"10","author":[{"given":"Yuelei","family":"Xu","sequence":"first","affiliation":[{"name":"Aeronautics Engineering College, Air Force Engineering University, Xi\u2019an 710038, Shanxi, China"},{"name":"Unmanned System Research Institute, Northwestern Polytechnical University, Xi\u2019an 710072, Shanxi, China"}]},{"given":"Mingming","family":"Zhu","sequence":"additional","affiliation":[{"name":"Aeronautics Engineering College, Air Force Engineering University, Xi\u2019an 710038, Shanxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7562-9220","authenticated-orcid":false,"given":"Shuai","family":"Li","sequence":"additional","affiliation":[{"name":"Aeronautics Engineering College, Air Force Engineering University, Xi\u2019an 710038, Shanxi, China"}]},{"given":"Hongxiao","family":"Feng","sequence":"additional","affiliation":[{"name":"Institute of Design and Research of Shaanxi Huanghe Group Co., Ltd, Xi\u2019an 710043, Shanxi, China"}]},{"given":"Shiping","family":"Ma","sequence":"additional","affiliation":[{"name":"Aeronautics Engineering College, Air Force Engineering University, Xi\u2019an 710038, Shanxi, China"}]},{"given":"Jun","family":"Che","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Aircraft Control, Flight Automatic Control Research Institute, Xi\u2019an 710065, Shanxi, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Trans. 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