{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T02:51:59Z","timestamp":1771037519598,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,13]],"date-time":"2018-06-13T00:00:00Z","timestamp":1528848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFB0503704"],"award-info":[{"award-number":["2017YFB0503704"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701410"],"award-info":[{"award-number":["41701410"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017M622550"],"award-info":[{"award-number":["2017M622550"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing","award":["16R02"],"award-info":[{"award-number":["16R02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Convolutional neural networks (CNNs) have been increasingly used in remote sensing scene classification\/recognition. The conventional CNNs are sensitive to the rotation of the image scene, which will inevitably result in the misclassification of remote sensing scene images that belong to the same category. In this work, we equip the networks with a new pooling strategy, \u201cconcentric circle pooling\u201d, to alleviate the above problem. The new network structure, called CCP-net can generate a concentric circle-based spatial-rotation-invariant representation of an image, hence improving the classification accuracy. The square kernel is adopted to approximate the circle kernels in concentric circle pooling, which is much more efficient and suitable for CNNs to propagate gradients. We implement the training of the proposed network structure with standard back-propagation, thus CCP-net is an end-to-end trainable CNNs. With these advantages, CCP-net should in general improve CNN-based remote sensing scene classification methods. Experiments using two publicly available remote sensing scene datasets demonstrate that using CCP-net can achieve competitive classification results compared with the state-of-art methods.<\/jats:p>","DOI":"10.3390\/rs10060934","type":"journal-article","created":{"date-parts":[[2018,6,15]],"date-time":"2018-06-15T10:15:14Z","timestamp":1529057714000},"page":"934","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Concentric Circle Pooling in Deep Convolutional Networks for Remote Sensing Scene Classification"],"prefix":"10.3390","volume":"10","author":[{"given":"Kunlun","family":"Qi","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingfeng","family":"Guan","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0406-7598","authenticated-orcid":false,"given":"Chao","family":"Yang","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feifei","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengyu","family":"Shen","sequence":"additional","affiliation":[{"name":"Soil and Water Conservation Department, Changjiang River Scientific Research Institute, Wuhan 430010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huayi","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3119","DOI":"10.1080\/01431160701469065","article-title":"An object-oriented approach for analysing and characterizing urban landscape at the parcel level","volume":"29","author":"Zhou","year":"2008","journal-title":"Int. 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