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DC Field | Value | Language |
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dc.contributor.author | Ning, H | - |
dc.contributor.author | Lei, T | - |
dc.contributor.author | An, M | - |
dc.contributor.author | Sun, H | - |
dc.contributor.author | Hu, Z | - |
dc.contributor.author | Nandi, AK | - |
dc.date.accessioned | 2023-06-11T11:30:06Z | - |
dc.date.available | 2023-06-11T11:30:06Z | - |
dc.date.issued | 2023-03-04 | - |
dc.identifier | ORCID iDs: Hailong Ning https://orcid.org/0000-0001-8375-1181; Asoke K. Nandi https://orcid.org/0000-0001-6248-2875. | - |
dc.identifier.citation | Ning, H. et al. (2023) 'Scale-wise interaction fusion and knowledge distillation network for aerial scene recognition', CAAI Transactions on Intelligence Technology, 0 (ahead-of-print), pp. 1 - 13. doi: 10.1049/cit2.12208. | en_US |
dc.identifier.issn | 2468-6557 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/26633 | - |
dc.description | Data availability statement: Data sharing is not applicable to this article as no new data were created or analysed in this study. | en_US |
dc.description.abstract | Copyright © 2023 The Authors. Aerial scene recognition (ASR) has attracted great attention due to its increasingly essential applications. Most of the ASR methods adopt the multi-scale architecture because both global and local features play great roles in ASR. However, the existing multi-scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features, leading to a limited ability to deal with challenges of large-scale variation and complex background in aerial scene images. In addition, existing methods may suffer from poor generalisations due to millions of to-be-learnt parameters and inconsistent predictions between global and local features. To tackle these problems, this study proposes a scale-wise interaction fusion and knowledge distillation (SIF-KD) network for learning robust and discriminative features with scale-invariance and background-independent information. The main highlights of this study include two aspects. On the one hand, a global-local features collaborative learning scheme is devised for extracting scale-invariance features so as to tackle the large-scale variation problem in aerial scene images. Specifically, a plug-and-play multi-scale context attention fusion module is proposed for collaboratively fusing the context information between global and local features. On the other hand, a scale-wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during training. Comprehensive experimental results show the proposed SIF-KD network achieves the best overall accuracy with 99.68%, 98.74% and 95.47% on the UCM, AID and NWPU-RESISC45 datasets, respectively, compared with state of the arts. | en_US |
dc.description.sponsorship | National Natural Science Foundation of China. Grant Numbers: 62201452, 2271296, 62201453; Natural Science Basic Research Program of Shaanxi. Grant Number: 2022JQ-592; Key Research and Development Program of Shaanxi Province. Grant Number: 2021JC-47; Shaanxi Provincial Education Department. Grant Number: 22JK0568. | en_US |
dc.format.extent | 1 - 13 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Wiley on behalf of The Institution of Engineering and Technology and Chongqing University of Technology | en_US |
dc.rights | Copyright © 2023 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | deep learning | en_US |
dc.subject | image analysis | en_US |
dc.subject | image classification | en_US |
dc.subject | information fusion | en_US |
dc.title | Scale-wise interaction fusion and knowledge distillation network for aerial scene recognition | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1049/cit2.12208 | - |
dc.relation.isPartOf | CAAI Transactions on Intelligence Technology | - |
pubs.issue | 00 | - |
pubs.publication-status | Published | - |
pubs.volume | 0 | - |
dc.identifier.eissn | 2468-2322 | - |
dc.rights.holder | The Authors | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2023 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. | 2.66 MB | Adobe PDF | View/Open |
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