A Feature Dynamic Enhancement and Global Collaboration Guidance Network for Remote Sensing Image Compression

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dc.contributor.authorFang, Q. Z.
dc.contributor.authorGu, S. B.
dc.contributor.authorWang, J. G.
dc.contributor.authorZhang, L. L.
dc.coverage.issue2cs
dc.coverage.volume34cs
dc.date.accessioned2025-05-19T07:09:35Z
dc.date.available2025-05-19T07:09:35Z
dc.date.issued2025-06cs
dc.description.abstractDeep learning-based remote sensing image compression methods show great potential, but traditional convolutional networks mainly focus on local feature extraction and show obvious limitations in dynamic feature learning and global context modeling. Remote sensing images contain multiscale local features and global low-frequency information, which are challenging to extract and fuse efficiently. To address this, we propose a Feature Dynamic Enhancement and Global Collaboration Guidance Network (FDEGCNet). First, we propose an Omni-Dimensional Attention Model (ODAM), which dynamically captures the key salient features in the image content by adaptively adjusting the feature extraction strategy to enhance the modelâ s sensitivity to key information. Second, a Hyperprior Efficient Attention Model (HEAM) is designed to combine multi-directional convolution and pooling operations to efficiently capture cross-dimensional contextual information and facilitate the interaction and fusion of multi-scale features. Finally, the Multi-Kernel Convolutional Attention Model (MCAM) integrates global branching to extract frequency domain context and enhance local feature representation through multi-scale convolutions. The experimental results show that FDEGCNet achieves significant improvement and maintains low computational complexity regarding image quality evaluation metrics (PSNR, MSSSIM, LPIPS, and VIFp) compared to the advanced compression models. Code is available at https://github.com/shiboGu12/FDEGCNeten
dc.formattextcs
dc.format.extent324-341cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2025 vol. 34, ÄŤ. 2, s. 324-341. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2025.0324en
dc.identifier.issn1210-2512
dc.identifier.urihttps://hdl.handle.net/11012/250926
dc.language.isoencs
dc.publisherRadioengineering Societycs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2025/25_02_0324_0341.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectRemote sensing image compressionen
dc.subjectconvolutional networksen
dc.subjectmultiscale convolutionen
dc.subjectattention modelen
dc.subjectmultiscale local featuresen
dc.subjectglobal low-frequency informationen
dc.titleA Feature Dynamic Enhancement and Global Collaboration Guidance Network for Remote Sensing Image Compressionen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikaÄŤnĂ­ch technologiĂ­cs
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