Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/25326
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, X | - |
dc.contributor.author | Zhang, Y | - |
dc.contributor.author | Lei, T | - |
dc.contributor.author | Wang, Y | - |
dc.contributor.author | Zhai, Y | - |
dc.contributor.author | Nandi, A | - |
dc.date.accessioned | 2022-10-17T13:29:08Z | - |
dc.date.available | 2022-10-17T13:29:08Z | - |
dc.date.issued | 2022-10-03 | - |
dc.identifier | ORCID iDs: Xuan Wang https://orcid.org/0000-0002-0842-6511; Tao Lei https://orcid.org/0000-0002-2104-9298; Yingbo Wang https://orcid.org/0000-0001-6447-8730; Asoke K. Nandi https://orcid.org/0000-0001-6248-2875. | - |
dc.identifier | 4941 | - |
dc.identifier.citation | Wang, X. et al (2022) 'Dynamic Convolution Self-Attention Network for Land-Cover Classification in VHR Remote-Sensing Images'. Remote Sensing, 14 (19), 4941, pp.1 - 20. https://doi.org/10.3390/rs14194941 | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/25326 | - |
dc.description | Data Availability Statement: The datasets used in this study have been published, and their addresses are https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-potsdam/ (accessed on 30 January 2021) and https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-vaihingen/ (accessed on 30 January 2021). | - |
dc.description.abstract | Copyright © 2022 by the authors. The current deep convolutional neural networks for very-high-resolution (VHR) remote-sensing image land-cover classification often suffer from two challenges. First, the feature maps extracted by network encoders based on vanilla convolution usually contain a lot of redundant information, which easily causes misclassification of land cover. Moreover, these encoders usually require a large number of parameters and high computational costs. Second, as remote-sensing images are complex and contain many objects with large-scale variances, it is difficult to use the popular feature fusion modules to improve the representation ability of networks. To address the above issues, we propose a dynamic convolution self-attention network (DCSA-Net) for VHR remote-sensing image land-cover classification. The proposed network has two advantages. On one hand, we designed a lightweight dynamic convolution module (LDCM) by using dynamic convolution and a self-attention mechanism. This module can extract more useful image features than vanilla convolution, avoiding the negative effect of useless feature maps on land-cover classification. On the other hand, we designed a context information aggregation module (CIAM) with a ladder structure to enlarge the receptive field. This module can aggregate multi-scale contexture information from feature maps with different resolutions using a dense connection. Experiment results show that the proposed DCSA-Net is superior to state-of-the-art networks due to higher accuracy of land-cover classification, fewer parameters, and lower computational cost. The source code is made public available. | en_US |
dc.description.sponsorship | National Natural Science Foundation of China (Program No. 61871259, 62271296, 61861024), in part by Natural Science Basic Research Program of Shaanxi (Program No. 2021JC-47), in part by Key Research and Development Program of Shaanxi (Program No. 2022GY-436, 2021ZDLGY08-07), in part by Natural Science Basic Research Program of Shaanxi (Program No. 2022JQ-634, 2022JQ-018), and in part by Shaanxi Joint Laboratory of Artificial Intelligence (No. 2020SS-03). | en_US |
dc.format.extent | 1 - 20 | - |
dc.format.medium | Electronic | - |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2022 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 (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | - |
dc.subject | Land-cover classification | en_US |
dc.subject | feature fusion | en_US |
dc.subject | self-attention | en_US |
dc.subject | lightweight | en_US |
dc.title | Dynamic Convolution Self-Attention Network for Land-Cover Classification in VHR Remote-Sensing Images | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/rs14194941 | - |
dc.relation.isPartOf | Remote Sensing | - |
pubs.issue | 19 | - |
pubs.publication-status | Published | - |
pubs.volume | 14 | - |
dc.identifier.eissn | 2072-4292 | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | Copyright © 2022 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 (https://creativecommons.org/licenses/by/4.0/). | 4.82 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License