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dc.contributor.authorYe, X
dc.contributor.authorXiong, F
dc.contributor.authorLu, J
dc.contributor.authorZhou, J
dc.contributor.authorQian, Y
dc.date.accessioned2020-12-23T03:25:29Z
dc.date.available2020-12-23T03:25:29Z
dc.date.issued2020
dc.identifier.issn2072-4292
dc.identifier.doi10.3390/rs12244027
dc.identifier.urihttp://hdl.handle.net/10072/400535
dc.description.abstractObject detection in remote sensing (RS) images is a challenging task due to the difficulties of small size, varied appearance, and complex background. Although a lot of methods have been developed to address this problem, many of them cannot fully exploit multilevel context information or handle cluttered background in RS images either. To this end, in this paper, we propose a feature fusion and filtration network (F3-Net) to improve object detection in RS images, which has higher capacity of combining the context information at multiple scales while suppressing the interference from the background. Specifically, F3-Net leverages a feature adaptation block with a residual structure to adjust the backbone network in an end-to-end manner, better considering the characteristics of RS images. Afterward, the network learns the context information of the object at multiple scales by hierarchically fusing the feature maps from different layers. In order to suppress the interference from cluttered background, the fused feature is then projected into a low-dimensional subspace by an additional feature filtration module. As a result, more relevant and accurate context information is extracted for further detection. Extensive experiments on DOTA, NWPU VHR-10, and UCAS AOD datasets demonstrate that the proposed detector achieves very promising detection performance.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherMDPI AG
dc.relation.ispartofpagefrom4027
dc.relation.ispartofissue24
dc.relation.ispartofjournalRemote Sensing
dc.relation.ispartofvolume12
dc.subject.fieldofresearchClassical Physics
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscience
dc.subject.fieldofresearchGeomatic Engineering
dc.subject.fieldofresearchcode0203
dc.subject.fieldofresearchcode0406
dc.subject.fieldofresearchcode0909
dc.titleℱ3-Net: Feature Fusion and Filtration Network for Object Detection in Optical Remote Sensing Images
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationYe, X; Xiong, F; Lu, J; Zhou, J; Qian, Y,ℱ3-Net: Feature Fusion and Filtration Network for Object Detection in Optical Remote Sensing Images, Remote Sensing, 2020, 12 (24), pp. 4027
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-12-23T03:20:42Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun


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